Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com
T-cell receptors (TCR) can recognize the intracellular targets whereas antibodies only recognize the 25% of potential extracellular targets
survivin is expressed in multiple cancers and correlates with poor survival and prognosis
CD3 bispecific TCR to survivn (Ab to CD3 on T- cells and TCR to survivin on cancer cells presented in MHC Class A3)
ABBV184 effective in vivo in lung cancer models as single agent;
in humanized mouse tumor models CD3/survivin bispecific can recruit T cells into solid tumors; multiple immune cells CD4 and CD8 positive T cells were found to infiltrate into tumor
therapeutic window as measured by cytokine release assays in tumor vs. normal cells very wide (>25 fold)
ABBV184 does not bind platelets and has good in vivo safety profile
First- in human dose determination trial: used in vitro cancer cell assays to determine 1st human dose
looking at AML and lung cancer indications
phase 1 trial is underway for safety and efficacy and determine phase 2 dose
survivin has very few mutations so they are not worried about a changing epitope of their target TCR peptide of choice
The discovery of TNO155: A first in class SHP2 inhibitor
SHP2 is an intracellular phosphatase that is upstream of MEK ERK pathway; has an SH2 domain and PTP domain
knockdown of SHP2 inhibits tumor growth and colony formation in soft agar
55 TKIs there are very little phosphatase inhibitors; difficult to target the active catalytic site; inhibitors can be oxidized at the active site; so they tried to target the two domains and developed an allosteric inhibitor at binding site where three domains come together and stabilize it
they produced a number of chemical scaffolds that would bind and stabilize this allosteric site
block the redox reaction by blocking the cysteine in the binding site
lead compound had phototoxicity; used SAR analysis to improve affinity and reduce phototox effects
was very difficult to balance efficacy, binding properties, and tox by adjusting stuctures
TNO155 is their lead into trials
SHP2 expressed in T cells and they find good combo with I/O with uptick of CD8 cells
TNO155 is very selective no SHP1 inhibition; SHP2 can autoinhibit itself when three domains come together and stabilize; no cross reactivity with other phosphatases
they screened 1.5 million compounds and got low hit rate so that is why they needed to chemically engineer and improve on the classes they found as near hits
In 2018 most of deals were in CART area but now we are seeing more series A rounds that are on novel mechanisms as well as rare diseases. US is still highest in venture capital series A but next is China. 10 of top ex US VC are from China, a whole lot of money.
Preclinical is very strong for US VC but China VC is focused on clinical. First time this year we see US series A break above 100. But ex US the series A is going down. Although preclinical deals in US is coming back not like as good as in 2006. But alot of > 1 billion $ deals. Most of money into mAbs and protein therapy; antisense is big and cell therapy is big too; small molecule not as much
ClearView Healthcare
Which innovation classes attracted VC in 2018?
Oncology drives a disproportionate focus could be driven by pharma focus on oncology; however there is some focus on neuro and infectious disease
therapeutic classes: shift to differentiated technology…. companies want technologic platforms not just drugs. Nucleic Acid tech and antibody tech is high need platforms. Startups can win by developing a strong platform not just a drug
There are pros and cons of developing a platform company versus a focused company. Many VCs have a portfolio and want something to fit in so look for a focused company and may not want a platform company. Pfizer feels that when alot of money is available (like now) platform investing is fine but when money becomes limited they will focus on those are what will be needed to fill therapy gaps. They believe buy the therapy and only rent the platform.
Merck does feel the way Pfizer does but they have separate ventures so they can look and license platforms. they are active in looking at companies with new modalities but they are focused on the money so they feel best kept in hands of biotech not pharma.
At Celgene they were solely focused on approvals not platforms. Alot of money is required to get these platforms to market. Concentration for platform companies should be the VCs not partnering or getting bought out by pharma. it seems from panel speakers from pharma that they are waiting for science to prove itself and waiting for favorable monetary environments (easy money). However it seems they (big pharma) are indicating that money is drying up or at least expect it too.
At Axial and with VCs they feel it is important to paint a picture or a vision at the early stage.
At Ontogeny, they focus on evaluating assets especially and most important, ThE MANAGEMENT TEAM. There are not that many great talented drug development management teams he feels out there even though great science out there.
Registration to this Virtual Event is free of charge and gives you 180 days access. You will be sent details of how to access the event via email after registration.
Academic Drug Discovery: Opportunities For Challenging Targets In Oncology Olivia Rossanese, Head of Biology and Reader, CR UK Cancer Therapeutics Unit, Institute of Cancer ResearchThe Cancer Research UK Cancer Therapeutics Unit at the Institute of Cancer Research is a multidisciplinary drug discovery group with a focus on novel, high-risk targets in cancer. This class of targets comes with unique challenges, such as a higher target validation burden or increased technical risk, but presents distinct opportunities for discovering novel biology and innovative medicines.
Academics and academic institutions need to pull together, to help generate more novel medicines for patients Chas Bountra, Chief Scientist, University of OxfordAcademics and academic institutions need to pull together, to help generate more novel medicines for patients. Together, we are creating a new ecosystem for drug discovery. One which we believe will accelerate the generation of more novel medicines, more quickly. We hope these will also be more affordable.
Chemical Proteomics: Accelerating Academic Target Discovery And Validation Edward Tate, Professor of Chemical Biology, Imperial College LondonMy group develops chemical biology approaches to identify and validate potential drug targets, particularly in the field of protein post-translational modification. In this talk I will present our recent research on developing chemical probes for protein lipidation, which has led to fundamental biological insights into the scope and roles of these modifications in disease biology, and to drug development projects in infection and cancer.
Institute For Applied Cancer Science, Developing The Next Generation Of Oncology Agents For Targeted Patient Populations Philip Jones, Head of Drug Discovery, Institute for Applied Cancer Science. MD Anderson Cancer Center, University of Texas MD Anderson Cancer CenterThe Institute for Applied Cancer Science (IACS) is a fully integrated drug discovery and development unit embedded within MD Anderson. By housing an innovative drug development operation within the world’s leading cancer center, we are surrounded by the culture of innovative academic science and clinical excellence for which MD Anderson is known.
Opening “The Box of Delights” – Accessing Pharma to Enhance Academic Drug Discovery Justin Bryans, Director of Drug Discovery, Medical Research Council TechnologyThis presentation will describe some of the ground breaking collaborations between MRC Technology and various Pharma companies, highlighting why these are so important to each party, and how these collaborations will drive innovative drug discovery and deliver new treatments to patients.
Small Molecules That Stabilize And Activate Lipoprotein Lipase (LPL) Mikael Elofsson, Professor, University of UmeåThe presentation covers a screening-based approach to identify small molecules that stabilize and activate lipoprotein lipase, a key enzyme in lipid metabolism. A medicinal program led to improved compounds with efficacy in vivo.
Crystal Resolution in Raman Spetctoscopy for Pharmaceutical Analysis, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Crystal Resolution in Raman Spetctoscopy for Pharmaceutical Analysis
Curator: Larry H. Bernstein, MD, FCAP
Investigating Crystallinity Using Low Frequency Raman Spectroscopy: Applications in Pharmaceutical Analysis
Crystallinity is an important factor when producing pharmaceuticals because it directly affects the bioavailability of the drug. Low-frequency Raman spectroscopy offers some advantages to the detection and analysis of crystallinity in pharmaceutical samples. Here the experimental requirements for low-frequency Raman measurements are described. The application of the technique to the study of crystallinity with a number of examples is discussed and the advantages and limitations are highlighted and compared with other techniques.
Raman spectroscopy is typically a nondestructive technique that uses lasers to probe for information about intra- and intermolecular bond vibrations. It involves irradiating the sample with monochromatic light to excite molecules within the sample to a virtually excited state. The molecules then relax to a higher (Stokes scattering) or lower (anti-Stokes scattering) vibrational level—resulting in the scattered light being lower or higher in frequency than the irradiating photons, respectively. Raman scattering is produced when this inelastic scattering occurs, and can be used to deduce information about the nature of the sample. Relative to the incident light, Raman scattering is a rare phenomenon, occurring in only 10-6 of the irradiated molecules (1). What occurs more commonly is elastic scattering, Rayleigh scattering, where the light emitted from the sample has the same energy as the incident light. The intensity of Raman scattering is determined by whether the vibrational mode of the molecule has a change in polarizability along its normal coordinate—similar to how infrared (IR) spectroscopy requires a changing dipole moment (1). For example, H2O has vibrational modes that have large dipole moment changes along the normal coordinate and as such it has strong IR bands. However, as H2O is an σ-bonded compound, the electrons have low polarizability and the change in polarizability with vibration is also low, hence the Raman scattering is weak. Conversely, the active pharmaceutical ingredients (APIs) in many drugs are generally π-bonded and easily polarizable, thus producing strong features in Raman spectra (2). This is one of the reasons why Raman spectroscopy is actively used in pharmaceutical studies.
Crystallinity and Why It Is Important for Pharmaceuticals
The term crystalline is used to describe solids in which the atoms or molecules are arranged in an ordered manner. For many pharmaceuticals, the crystalline form is more kinetically stable than the amorphous form, which typically results in crystalline solids being less soluble, and therefore less bioavailable than their amorphous counterparts. The influence that crystallinity has over the solubility of a solid is what makes this an important factor when manufacturing pharmaceuticals, as those which are supposed to be fast-acting should be readily soluble in the body, while slow-acting drugs should dissolve relatively slowly. This has been shown to be the case in previous studies of bioavailability of APIs in the literature (3–5). Because of this, it is important to be able to control the crystallinity of a drug and monitor it. However, the crystallinity of a sample cannot be assumed to be simply either 100% ordered or 100% amorphous. Instead, the literature has indicated that disorder occurs along a continuous scale where a sample can gradually become more or less crystalline before becoming fully ordered or disordered (6). Crystallinity can be controlled in a number of different ways. One such method involves creating a fully crystalline pharmaceutical product and introducing disorder into the structure mechanically by milling the sample (4,5). Previous studies have also shown that amorphous pharmaceuticals can be produced through different drying methods (7,8). Even accidental adjustment of properties such as moisture level can lead to a change in crystallinity (9,10). Methods to monitor the crystallinity of a sample are therefore useful.
Established methods for monitoring crystallinity include calorimetry and X-ray diffraction (XRD) (6). Terahertz spectroscopy is arguably a more recently implemented method of analysis of crystallinity and is the only one of these three techniques described in any detail here. The first terahertz technique described is terahertz absorption spectroscopy or far-IR spectroscopy. The terahertz radiation has a wavelength that is between that of IR and microwaves (0.1–1 mm or 10–100 cm-1) and has the ability to allow observation of various low energy vibrations within the sample (11–13). Vibrations can essentially be divided into two categories when studying crystalline samples: external and internal vibrational modes. Internal vibrations can be considered the local vibrations that occur within each molecule (that is, intramolecular bond vibrations). External vibrations can be considered the vibrations involving the overall lattice of the crystal, such as phonon modes or torsional vibrations (14,15). Terahertz spectroscopy tends to focus on the external vibrations. The technique has been known for more than 100 years, and two experimental challenges have inhibited its widespread use. The first of these is the presence of strong water absorption above 60 cm-1, which has complicated analysis of wet samples (16) and the second is the presence of ambient terahertz radiation that compromises spectral detection (17). The second of these issues has been solved in large part with the advent of terahertz time-domain spectroscopy, also known as terahertz pulsed spectroscopy (TPS), which generates terahertz radiation using femtosecond laser pulses, using a laser wavelength of around 780 nm. For more in-depth detail about terahertz spectroscopy, refer to references 11–13. Despite its relative underutilization, this technique has been found to be an effective method for observing and even quantifying crystallinity in various papers in the literature. Model compounds such as cellulose and gelatin–amino acid mixtures have been used previously to determine the efficacy of the technique when compared to XRD (18) or to determine whether it would be an efficient method for in-line and off-line analysis of pharmaceutical crystallinity (19). In particular, the use of terahertz spectroscopy with a multivariate analysis technique—partial least squares (PLS)—permitted the quantification of the crystallinity index (CI) of cellulose (18). However, not only has the crystallinity of model systems been analyzed using the technique, drugs such as indomethacin, ketoprofen, carbamazepine, enalapril maleate, fenoprofin, irbesartan, and diclofenac acid have also been studied (11,19–23). The use of PLS also proved effective when working with pharmaceutical products, allowing not only real formulations to be distinguished from one another, but also the different crystalline forms to be distinguished. These forms were indicated by Strachan and colleagues to include polymorphic, liquid crystalline, and amorphous states (11,24). Therefore, terahertz spectroscopy has seen growing interest based on its success with the study of pharmaceuticals. Other reviews have also come to compare terahertz spectroscopy with other forms of vibrational spectroscopy for pharmaceutical analysis (25,26).
Low-Frequency Raman
Raman spectroscopy has been used for decades to analyze and characterize polymorphs (27,28); however, like terahertz spectroscopy, low-frequency Raman spectra have been used more recently (29–31). Mid-frequency and high-frequency regions of Raman spectra are typically used to analyze the intramolecular bonding of molecules within a sample (internal vibrations). However, low-frequency Raman spectroscopy involves the analysis of the low-frequency regions of Raman spectra, which tend to contain features attributable to external vibrations of the crystalline lattice (14). Frequencies in the same region as those observed with terahertz spectroscopy can be observed using standard continuous wave lasers. Because Raman scattering is such a rare process compared to Rayleigh scattering, the Rayleigh signal must be removed before recording spectra. Many ways of doing this have been developed. Until the 1990s it was most commonly performed by the use of a triple-grating spectrograph in conjunction with a photomultiplier tube (32,33). This method is reliable but because of the many mirrors and diffraction gratings necessary, the ultimate efficiency or throughput of the system is inevitably low. To compensate for this, high laser powers and long acquisition times are needed (30). A less commonly used method involves the stabilization of iodine gas at specific temperatures to allow for the absorption of the unwanted laser signal (34). However, these methods are cumbersome and it later became more common to use holographic notch or edge filters that reject light with a specific wavelength. These filters can be designed to reject nearly any wavelength of light to match that of the laser used while allowing the rest of the spectrum to pass through. This method allows for the use of more efficient single grating spectrographs. In conjunction with array detectors or charge coupled devices (CCDs) an entire Raman spectrum can be acquired simultaneously. However, the filters also block a portion of the Raman scattering signal in addition to the Rayleigh line. In most cases, the spectra can only be collected above about 100 cm-1 (35).
Over the past decade new filtering technologies have been developed that allow for the acquisition of Raman spectra reaching very close to the laser frequency (35) while using standard dispersive Raman systems with all of their advantages. These filters are typically based on holographic reflective volume Bragg gratings (VBGs) and can achieve frequencies as low as 5 cm-1(34).
In most cases low-frequency Raman is measured using near-IR (NIR) diode laser sources for excitation. This is because the selectivity of VBGs decreases as wavelength decreases. Therefore, it is easier to acquire low-frequency Raman data with longer-wavelength lasers (34).
An issue that arises when performing low-frequency Raman measurements are artifacts caused by the laser itself (34). These may not be apparent when standard holographic filters are used because they appear so close to the laser line and would normally be blocked. These artifacts arise from laser instability, temperature fluctuations, amplified spontaneous emission, and plasma lines. VBG filters are therefore also used to clean up the laser line before it is focused onto the sample. In some optical designs a single VBG is used for both purposes simultaneously (34,35).
The resulting spectrum then allows detection of crystalline or amorphous forms, as the low-frequency Raman bands are associated with the low energy bond vibrations, hydrogen bonds, and phonon modes. Therefore, the overall environment and arrangement of the molecules will have a considerable effect on these vibrations. If the sample is crystalline, then the molecules will be highly ordered, and the low energy bond vibrations will show up in the spectrum as sharp peaks because the bonds will have a very similar environment. Conversely, if the sample is amorphous, there will be very little order and there will be a wide variety of molecular environments which, in turn, will typically produce only one broad band, known as the boson peak, and no other low-frequency features (36). Illustrations of crystalline and amorphous spectra are provided later in this article. This distinct difference between crystalline and amorphous materials is what suggests that low-frequency Raman spectroscopy could prove to be extremely useful in the study of pharmaceuticals.
Analysis
Overview of the Setup of a Low-Frequency Raman System
The exemplar data presented in this article were collected using two different low-frequency Raman systems, the first of which (Figure 1) is a home-built system based on a wavelength-stabilized 80-mW, 785-nm laser module (Ondax, Inc). The laser line is initially filtered by use of two BragGrate reflective VBGs (OptiGrate Corp.) to remove amplified spontaneous emission. The sample is arranged in a 135° backscattering geometry relative to a collection lens (f/2.3). The collected light is passed through a pair of VBGs (OptiGrate Corp. BragGrate 785 nm, OD3). The collimated, filtered light is then focused by a second f/2.3 lens onto a fiber-optic cable. The cable is coupled to an LS 785 spectrograph (Princeton Instruments). A third VBG is used to further filter light imaged onto the entrance slit of the spectrograph. Detection is achieved using a CCD (Princeton Instruments thermoelectrically cooled PIXIS 100 BR CCD). A typical spectral range for this experiment is about 2400 cm-1. Both the Stokes and anti-Stokes region of the spectrum can be seen in addition to a small remaining laser line signal. Raman standards such as sulfur and 1,4-bis(2-methylstyryl)benzene (BMB) are used to calibrate the spectrometer (37).
Figure 1: Illustration of an exemplar low-frequency Raman setup with a 785-nm laser.
The second system is based on a pre-built SureBlock XLF-CLM THz-Raman system from Ondax Inc. The laser (830 nm, 200 mW), cleanup filters, and laser line filters are all self-contained inside of the instrument but operate on the same principles as the 785-nm system. The sample is arranged in a 180° backscattering geometry relative to a 10× microscope lens. This system is then coupled via a fiber-optic cable to a Princeton Instruments SP2150i spectrograph and PIXIS 100 CCD camera. The 0.15-m spectrograph is used in conjunction with either a 1200- or 1800-groove/mm blazed diffraction grating to adjust the resolution and spectral range.
Crystalline Versus Amorphous Samples
The Raman spectrum of crystalline and amorphous solids differ greatly in the low-frequency region (see Figure 2) because of the highly ordered and highly disordered molecular environments of the respective solids. However, the mid-frequency region can also be noticeably altered by the changing environment (Figure 3).
A potential issue is optical artifacts, and these may be identified by the analysis of both Stokes and anti-Stokes spectra. One advantage of the experimental setups described is that signal from the sample may be measured within minutes and it is nondestructive, thus allowing Raman spectra to be collected from a single sample using both techniques at virtually the same time. This approach permits the examination of low-frequency Raman data with 785-nm and 830-nm excitation and allows comparison with Fourier transform (FT)-Raman spectra, in which it is possible to collect meaningful data down to a Raman shift of 50 cm-1. The benefits are demonstrated in Figure 4. In this data, each technique produces consistent bands with similar Raman shifts and relative intensities. While Raman data were not collected below 50 cm-1 using the 1064-nm system, the bands at 69 and 96 cm-1 are consistent with the 785- and 830-nm data. Furthermore, the latter two methods show consistency with bands appearing around 32 and 46 cm-1 for both techniques.
Figure 4: Comparison of the low-frequency region of three Raman spectroscopic techniques.
Case Studies
So far there have been few studies to utilize low-frequency Raman spectroscopy in the analysis of pharmaceutical crystallinity. Despite this, the literature does contain articles that demonstrate the promising applicability of the technique.
Mah and colleagues (38) studied the level of crystallinity of griseofulvin using low-frequency Raman spectroscopy with PLS analysis. In this study a batch of amorphous griseofulvin (which was checked using X-ray powder diffractometry) was prepared by melting the griseofulvin and rapidly cooling it again using liquid nitrogen. Condensed water was removed by placing the sample over phosphorus pentoxide and the glassy sample was then ground using mortar and pestle. Calibrated samples of 2%, 4%, 6%, 8%, and 10% crystallinity were then created though geometric mixing of the amorphous and crystalline samples; following this mixing, the samples were then pressed into tablets. Many tablets were then stored in differing temperatures (30 °C, 35 °C, and 40 °C) at 0% humidity. Low-frequency 785-nm, mid-frequency 785-nm, and FT-Raman spectroscopies were performed simultaneously on each sample. After PLS analysis, limits of detection (LOD) and limits of quantification (LOQ) were calculated. The results of this research showed that each of these three techniques were capable of quantifying crystallinity. It also showed that FT-Raman and low-frequency Raman techniques were able to both detect and quantify crystallinity earlier than the mid-frequency 785 nm Raman technique. The respective LOD and LOQ values for FT-Raman, low-frequency Raman, and mid-frequency Raman are as follows: LOD values: 0.6%, 1.1%, and 1.5%; LOQ values: 1.8%, 3.4%, and 4.6%. The root mean squared errors of prediction (RMSEP) were also calculated and, like the LOD and LOQ values, indicated that the FT-Raman data had the lowest error, followed by the low-frequency Raman, and mid-frequency Raman had the largest errors of the three techniques. The recrystallization tests that were performed indicated that higher temperatures showed a distinct increase in the rate of recrystallization and that each technique provided similar results (within experimental error). It is also important to note that each technique gave similar spectra (where applicable), which provides supporting evidence that the data is meaningful. Overall, the conclusions of this research were that low-frequency predictions of crystallinity are at least as accurate as the predictions made using mid-frequency Raman techniques. It is arguable that low-frequency Raman is better because of the presence of stronger spectral features and because they are intrinsically linked with crystallinity.
Hédoux and colleagues (36) investigated the crystallinity of indomethacin using low-frequency Raman spectroscopy and compared the results with high frequency data. The ranges of interest were indicated to be 5–250 cm-1and 1500–1750 cm-1 regions. Samples of indomethacin were milled using a cryogenic mill to avoid mechanical heating of the sample, with full amorphous samples being obtained after 25 min of milling. Methods used in this study include Raman spectroscopy, isothermal differential scanning calorimetry (DSC), and X-ray diffractometry as well as the milling technique. The primary objective of this research was to use all of these techniques to monitor the crystallization of amorphous indomethacin to the more stable γ-state while the sample was at room temperature–well below the glass transition temperature,Tg = 43 °C. The results of this research did in fact show that low-frequency Raman spectroscopy is a very sensitive technique for identifying very small amounts of crystallinity within mostly amorphous samples. The data was supported by the well-established methods for monitoring crystallinity: XRD and DSC. This paper particularly noted the benefit of low acquisition times associated with low-frequency Raman spectroscopy compared with the other techniques used.
Low-frequency Raman spectroscopy was also used to monitor two polymorphic forms of caffeine after grinding and pressurization of the samples (39). Pressurization was performed hydrostatically using a gasketed membrane diamond anvil cell (MDAC), while ball milling was used as the method of grinding the sample. Analysis methods used were low-frequency Raman and X-ray diffraction. Low-frequency Raman spectra revealed that, upon slight pressurization, caffeine form I transforms into a metastable state slightly different from that of form II and that a disordered (amorphous) state is achieved in both forms when pressurized above 2 GPa. In contrast, it is concluded that grinding results in the transformation of each form into the other with precise grinding times, thus also generating an intermediate form, which was found to only be observable using low-frequency Raman spectroscopy. The caffeine data, as well as the low-frequency data obtained for indomethacin were further discussed by Hédoux and colleagues (40).
Larkin and colleagues (41) used low-frequency Raman in conjunction with other techniques to characterize several different APIs and their various forms. The other techniques include FT-Raman spectroscopy, X-ray powder diffraction (XRPD), and single-crystal X-ray diffractometry. The APIs studied include carbamazepine, apixaban diacid co-crystals, theophylline, and caffeine and were prepared in various ways that are not detailed here. During this research, low-frequency Raman spectroscopy played an important role in understanding the structures while in their various forms. However, more importantly, low-frequency Raman spectroscopy produced information-rich regions below 200 cm-1 for each of the crystalline samples and noticeably broad features when the APIs were in solution.
Wang and colleagues (42) investigated the applicability of low-frequency Raman spectroscopy in the analysis of respirable dosage forms of various pharmaceuticals. The analyzed pharmaceuticals were involved in the treatment of asthma or chronic obstructive pulmonary disease (COPD) and include salmeterol xinafoate, formoterol fumarate, glycopyrronium bromide, fluticasone propionate, mometasone furoate, and salbutamol sulfate. Various formulations of amino acid excipients were also analyzed in this study. Results indicated that the use of low-frequency Raman analysis was beneficial because of the large features found in the region and allowed for reliable identification of each of the dosage forms. Not only this, it also allowed unambiguous identification of two similar bronchodilators, albuterol (Ventolin) and salbutamol (Airomir).
Heyler and colleagues (43) collected both the low-frequency and fingerprint region of Raman spectra from several polymorphs of carbamazepine, an anticonvulsant and mood stabilizer. This study found that the different polymorphs of this API could be distinguished effectively using these two regions. Similarly, Al-Dulaimi and colleagues (44) demonstrated that polymorphic forms of paracetamol, flufenamic acid, and imipramine hydrochloride could be screened using low-frequency Raman and only milligram quantities of each drug. In this study, paracetamol and flufenamic acid were used as the model compounds for comparison with a previously unstudied system (imipramine hydrochloride). Features within the low-frequency Raman regions of spectra were shown to be significantly different between forms of each drug. Therefore this study also indicated that the polymorphs were highly distinguishable using the technique. Hence, like all other previously mentioned case studies, these investigations further demonstrate the utility of low-frequency Raman spectroscopy as a fast and effective method for screening pharmaceuticals for crystallinity.
Conclusions
Low-frequency Raman spectroscopy is a new technique in the field of pharmaceuticals, as well as in general studies of crystallinity. This is despite indications in previous studies showing an innate ability of the technique for identifying crystalline materials and in some cases, quantifying crystallinity. Arguably one of the most beneficial aspects of this technique is the relatively small amount of time necessary to prepare and analyze samples when compared with XRD or DSC. This should ensure the growing use of low-frequency Raman spectroscopy in, not only pharmaceutical crystallinity studies, but also crystallinity studies of other substances as well.
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The concept of group wavenumbers is defined, and the importance of recognizing patterns in infrared (IR) spectra is discussed. Continuing our theme of investigating the IR spectra of hydrocarbons, we look at the nature of aromatic bonding and why aromatic rings have unique structures, bonding, and IR spectra. The IR spectrum of benzene is analyzed in detail as a prototype example of an aromatic hydrocarbon.
A look at the spectrum of any pure molecule will disclose that many functional groups have multiple peaks. It is this pattern of peaks that defines the presence of a specific functional group in a sample, not one specific peak. Thus, interpreting spectra is not about memorizing peak positions, but is instead an exercise in pattern recognition. The human brain has evolved to be great at pattern recognition. Computers are good at many things except pattern recognition, hence the need to use our eyeballs and brains to interpret infrared (IR) spectra. A computer’s inability to interpret spectra is part of why there is a need for a column series like this one. Since humans are good pattern recognizers, I believe most people can learn to interpret IR spectra. My approach going forward will be to emphasize the pattern of peaks that defines the presence of a functional group in a sample rather than throwing hundreds of peak positions at you.
The peaks that define a functional group are what I will call its group wavenumbers. Some people call these “group frequencies,” but this is a misnomer. The x-axes of IR spectra are plotted in wavenumber, not frequency. A good group wavenumber peak has three useful properties:
It will be intense so it is easy to see.
It will appear in a unique wavenumber region where no other functional groups absorb.
It will fall in a narrow wavenumber range regardless of what molecule the functional group appears in.
We have talked about group wavenumbers and peak patterns in previous articles without realizing it. For example, the methyl and methylene C-H stretches that were introduced previously (1). Now, we have given these peak patterns a proper name.
To be clear, not all of the peaks from a given functional group will be useful group wavenumbers. For example, a peak may be too weak to be seen reliably, may appear in a region where lots of other peaks appear, or move around a lot from molecule to molecule. Many of the upcoming columns will be devoted to the useful group wavenumbers of economically important functional groups.
One final thought: I compare interpreting IR spectra to playing a piano. You can’t just walk up to a Steinway and play a Beethoven sonata, you have to practice first. Similarly, you can’t just walk up to a spectrum and pull information out of it, you have to practice first. The purpose of these columns is to give you the knowledge you need to interpret spectra and the problem spectra give you the opportunity to practice what you have learned.
Introduction to Aromatic Molecules
To be able to interpret IR spectra, one must have a nodding familiarity with the nomenclature and structures of organic chemistry. In today’s world many people interpreting spectra do not have this background, so I have and will continue to discuss the structure and nomenclature of functional groups whose spectra we will discuss.
Aromatic molecules were originally named because some of them smell nice. However, if you have ever smelled pyridine you know that not all aromatic molecules do smell nice. It has been found that the bonding in aromatic molecules is unique.
The prototypical aromatic molecule is benzene, C6H6, whose structure is represented in Figure 1.
Figure 1: The chemical structure of the benzene molecule, C6H6.
The drawing in Figure 1 is that of a six-membered ring or hexagon. A carbon atom is located at each vertex of the hexagon and a hydrogen atom is attached to each carbon, although it is not written in. The circle inside the ring represents that the electrons are delocalized which is illustrated in Figure 2.
Figure 2: Top: The P orbitals on each of the six carbon atoms in benzene that contribute an electron to the ring. Bottom: the collection of delocalized P orbital electrons forming a cloud of electron density above and below the benzene ring.
Each of the carbon atoms in a benzene ring contains two P orbitals containing a lone electron, and one of these orbitals is perpendicular to the benzene ring as seen in the top of Figure 2. There is enough orbital overlap that these electrons, rather than being confined between two carbon atoms as might be expected, instead delocalize and form clouds of electron density above and below the plane of the ring. This type of bonding is called aromatic bonding(2), and a ring that has aromatic bonding is called an aromatic ring. It is aromatic bonding that gives aromatic rings their unique structures, chemistry, and IR spectra. Benzene is simply a commonly found aromatic ring. Other types of aromatic molecules include polycyclic aromatic hydrocarbons (PAHs), such as naphthalene, that contain two or more benzene rings that are fused (which means adjacent rings share two carbon atoms), and heterocyclic aromatic rings which are aromatic rings that contain a noncarbon atom such as nitrogen. Pyridine is an example of one of these. The interpretation of the IR spectra of these latter aromatic molecules will be discussed in future articles.
Figure 3: The IR spectrum of benzene, measured as a capillary thin film between two KBr windows.
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The area labeled B in Figure 3 refers to a region in aromatic ring spectra called the summation bands. More information on these peaks will come in a later column. The label C in Figure 3 at 1478 cm-1 is an example of a ring mode peak. A ring mode is a vibration that involves the stretching and contracting of the carbon-carbon bonds in an aromatic ring. These are typically sharp, but vary in number and intensity depending upon the molecule. They fall between 1620 and 1400 cm-1 as stated in Table I. The region labeled D in Figure 3 is where aromatic ring C-H in-plane bending peaks fall. These peaks are generally medium to weak in intensity, show up in a very busy spectral region, and hence are not useful group wavenumbers.
The most intense peak in Figure 3, labeled E, is an out-of-plane C-H bend. Since aromatic rings are planar, all the hydrogens are in the plane of the molecule. When these hydrogens bend above and below the plane of the molecule they are undergoing a C-H out-of-plane bend, which is sometimes called a wag because of the vibration’s resemblance to the wagging of a dog’s tail. This vibration gives rise to a large peak that typically falls between 1000 cm-1 and 700 cm-1. In the spectrum of benzene, this peak falls at 674 cm-1 because the molecule is unsubstituted.
Conclusion
To review then, the useful group wavenumbers for benzene rings are one or more C-H stretches between 3100 and 3000 cm-1, one or more sharp ring modes between 1620 and 1400 cm-1, and an intense ring bend from 1000 to 700 cm-1. Most of the time when a benzene ring is encountered it contains one or more substituents. The IR spectra of mono- and disubstituted benzene rings will be the topic of the next installment of this column.
Super-Resolution Fluorescence Microscopy: Where To Go Now? Bernd Rieger, Quantitative Imaging Group Leader, Delft University of Technology
09:30
Keynote Presentation
From Molecules To Whole Organs Francesco Pavone, Principal Investigator, LENS, University of Florence
Some examples of correlative microscopies, combining linear and non linear techniques will be described. Particular attention will be devoted Alzheimer disease or to neural plasticity after damage as neurobiological application.
10:15
Super-Resolution Imaging by dSTORM Markus Sauer, Professor, Julius-Maximilians-Universität Würzburg
10:45
Coffee and Networking in Exhibition Hall
11:15
Correlated Fluorescence And X-Ray Tomography: Finding Molecules In Cellular CT Scans Carolyn Larabell, Professor, University of California San Francisco
11:45
Integrating Advanced Fluorescence Microscopy Techniques Reveals Nanoscale Architecture And Mesoscale Dynamics Of Cytoskeletal Structures Promoting Cell Migration And Invasion Alessandra Cambi, Assistant Professor, University of Nijmegen
This lecture will describe our efforts to exploit and integrate a variety of advanced microscopy techniques to unravel the nanoscale structural and dynamic complexity of individual podosomes as well as formation, architecture and function of mesoscale podosome clusters.
12:15
Multi-Photon-Like Fluorescence Microscopy Using Two-Step Imaging Probes George Patterson, Investigator, National Institutes of Health
12:45
Lunch & Networking in Exhibition Hall
14:15
Technology Spotlight
14:30
3D Single Particle Tracking: Following Mitochondria in Zebrafish Embryos Don Lamb, Professor, Ludwig-Maximilians-University
15:00
Visualizing Mechano-Biology: Quantitative Bioimaging Tools To Study The Impact Of Mechanical Stress On Cell Adhesion And Signalling Bernhard Wehrle-Haller, Group Leader, University of Geneva
15:30
Superresolution Imaging Of Clathrin-Mediated Endocytosis In Yeast Jonas Ries, Group Leader, EMBL Heidelberg
We use single-molecule localization microscopy to investigate the dynamic structural organization of the east endocytic machinery. We discovered a striking ring-shaped pre-patterning of the actin nucleation zone, which is key for an efficient force generation and membrane invagination.
16:00
Coffee and Networking in Exhibition Hall
16:30
Optical Imaging of Molecular Mechanisms of Disease Clemens Kaminski, Professor, University of Cambridge
17:00
3-D Optical Tomography For Ex Vivo And In Vivo Imaging James McGinty, Professor, Imperial College London
17:30
End Of Day One
Wednesday, 15 June 2016
09:00
Imaging Gene Regulation in Living Cells at the Single Molecule Level James Zhe Liu, Group Leader, Janelia Research Campus, Howard Hughes Medical Institute
09:30
Keynote Presentation
Super-Resolution Microscopy With DNA Molecules Ralf Jungmann, Group Leader, Max Planck Institute of Biochemistry
10:15
A Revolutionary Miniaturised Instrument For Single-Molecule Localization Microscopy And FRET Achillefs Kapanidis, Professor, University of Oxford
10:45
Coffee and Networking in Exhibition Hall
11:15
Democratising Live-Cell High-Speed Super-Resolution Microscopy Ricardo Henriques, Group Leader, University College London
Information In Localisation Microscopy Susan Cox, Professor, Kings College London
12:45
Lunch & Networking in Exhibition Hall
14:15
Technology Spotlight
14:30
High-Content Imaging Approaches For Drug Discovery For Neglected Tropical Diseases Manu De Rycker, Team Leader, University of Dundee
The development of new drugs for intracellular parasitic diseases is hampered by difficulties in developing relevant high-throughput cell-based assays. Here we present how we have used image-based high-content screening approaches to address some of these issues.
15:00
High Resolution In Vivo Histology: Clinical in vivo Subcellular Imaging using Femtoseceond Laser Multiphoton/CARS Tomography Karsten König, Professor, Saarland University
We report on a certified, medical, transportable multipurpose nonlinear microscopic imagingsystem based on a femtosecond excitation source and a photonic crystal fiber with multiple miniaturized time-correlated single-photon counting detectors.
15:30
Coffee and Networking in Exhibition Hall
16:00
Lateral Organization Of Plasma Membrane Constituents At The Nanoscale Gerhard Schutz, Professor, Vienna University of Technology
It is of interest how proteins are spatially distributed over the membrane, and whether they conjoin and move as part of multi-molecular complexes. In my lecture, I will discuss methods for approaching the two questions, and provide biological examples.
16:30
Correlative Light And Electron Microscopy In Structural Cell Biology Wanda Kukulski, Group Leader, University of Cambridge
Mid Atlantic LRIG 22nd Annual Technology Showcase: Agenda on 3D Bioprinting on Wednesday, May 11, 2016 at Holiday Inn, 195 Davidson Avenue, Somerset, NJ
Reporter: Stephen J. Williams, Ph.D.
Symposium Speakers and Topics:
Human Organoids
Hatem E. Sabaawy-Director, Production GMP Facility for Cell and Gene Therapy, RBHS-Robert Wood Johnson Medical School, Rutgers Cancer Institute of New Jersey
Intestinal Organoids for Drug Discovery Richard Visconti-Associate Principal Scientist, Cellular Pharmacology, Merck Research Laboratories, Kenilworth, New Jersey
3D Bioprinting Elizabeth Wu-President, WuZenTech, Edison, New Jersey
Building Your Brand Through LinkedIn
Stan Robinson, Jr., LinkedIn Consultant, Helping Professionals with Social Selling, Personal Branding
Optimized sgRNA Libraries for Genetic Screens with CRISPR-Cas9 John Doench, Ph.D., Associate Director, Genetic Perturbation Platform, Broad Institute of Harvard and MIT
Optimizing CRISPR for Pooled Genome-Wide Functional Genetic Screens Paul Diehl, Ph.D., Director, Business Development, Cellecta, Inc.
CRISPR-Cas9 Whole Genome Screening: Going Where No Screen Has Gone Before Ralph Garippa, Ph.D., Director, RNAi Core Facility, Sloan-Kettering Institute, Memorial Sloan-Kettering Cancer Center
Cross-Species Synthetic Lethal Screens and Applications to Drug Discovery Norbert Perrimon, Ph.D., Professor, Department of Genetics, Harvard Medical School and Investigator, Howard Hughes Medical Institute
Interactive Breakout Discussion Groups with Continental Breakfast This session features various discussion groups that are led by a moderator/s who ensures focused conversations around the key issues listed. Attendees choose to join a specific group and the small, informal setting facilitates sharing of ideas and active networking. Continental breakfast is available for all participants.
Topic: CRISPR/Cas9 System for In vivo Drug Discovery Moderator: Danilo Maddalo, Ph.D., Lab Head, ONC Pharmacology, Novartis Institutes for BioMedical Research
Impact of CRISPR/Cas9 system on in vivo mouse models
Application of the CRISPR/Cas9 system in in vivo screens
Technical limitations/safety issues
Topic: Getting Past CRISPR Pain Points Moderators: John Doench, Ph.D., Associate Director, Genetic Perturbation Platform, Broad Institute of Harvard and MITStephanie Mohr, Ph.D., Lecturer, Genetics & Director of the Drosophila RNAi Screening Center, Harvard Medical School
Challenges and solutions for CRISPR gRNA design
Methods for detecting engineered changes
Topic: Cellular Delivery of CRISPR/Cas9 Moderator: Daniel E Bauer M.D., Ph.D., Assistant Professor of Pediatrics, Harvard Medical School and Staff Physician in Pediatric Hematology/Oncology, Boston Children’s Hospital and Dana-Farber Cancer Institute, Principal Faculty, Harvard Stem Cell Institute
GENE EDITING FOR SCREENING DISEASE PATHWAYS AND DRUG TARGETS
Scouring the Non-Coding Genome by Saturating Edits Daniel E. Bauer, M.D., Ph.D., Assistant Professor of Pediatrics, Harvard Medical School and Staff Physician in Pediatric Hematology/Oncology, Boston Children’s Hospital and Dana-Farber Cancer Institute, Principal Faculty, Harvard Stem Cell Institute
Parallel shRNA and CRISPR/Cas9 Screens Reveal Biology of Stress Pathways and Identify Novel Drug Targets Michael Bassik, Ph.D., Assistant Professor, Department of Genetics, Stanford University
BUILDING THE CRISPR TOOLBOX
Beyond Cas9: Discovering Single Effector CRISPR Tools Jonathan Gootenberg, Member, Laboratories of Dr. Aviv Regev and Dr. Feng Zhang, Department of Systems Biology, Harvard Medical School, and Broad Institute of Harvard and MIT
CRISPR-Cas9 Genome Editing Improves Sub-Cellular Localization Studies Netanya Y. Spencer, M.D., Ph.D., Research Fellow in Medicine, Joslin Diabetes Center, Harvard Medical School
TECHNOLOGY PANEL: Trends in CRISPR Technologies Panelists to be Announced
This panel will bring together 2-3 technical experts from leading technology and service companies to discuss trends and improvements in CRISPR libraries, reagents and platforms that users can expect to see in the near future. (Opportunities Available for Sponsoring Panelists)
APPLICATIONS OF CRISPR FOR DRUG DISCOVERY
Use of CRISPR and Other Genomic Technologies to Advance Drug Discovery Namjin Chung, Ph.D., Head, Functional Genomics Platform, Discovery Research, AbbVie, Inc.
Application of Genome Editing Tools to Model Human Genetics Findings in Drug Discovery Myung Shin, Ph.D., Senior Principal Scientist, Genetics and Pharmacogenomics, Merck & Co. Inc.
In vivo Application of the CRISPR/Cas9 Technology for Translational Research Danilo Maddalo, Ph.D., Lab Head, ONC Pharmacology, Novartis Institutes for BioMedical Research
DEVELOPING TOOLS FOR BETTER TRANSLATION
Improving CRISPR-Cas9 Precision through Tethered DNA-Binding Domains Scot A. Wolfe, Ph.D., Associate Professor, Department of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School
Nucleic Acid Delivery Systems for RNA Therapy and Gene Editing Daniel G. Anderson, Ph.D., Professor, Department of Chemical Engineering, Institute for Medical Engineering & Science, Harvard-MIT Division of Health Sciences & Technology and David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology
Translating CRISPR/Cas9 into Novel Medicines Alexandra Glucksmann, Ph.D., COO, Editas Medicine
2nd Annual Translational Gene Editing: Exploiting CRISPR/Cas9 for Building Tools for Drug Discovery & Development: June 16, 2016, Boston, MA, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
GEN Tech Focus: Rethinking Gene Expression Analysis, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
GEN Tech Focus: Rethinking Gene Expression Analysis
Larry H. Bernstein, MD, FCAP, Curator
LPBI
Quantitating gene expression is essential for researchers to answer important biological questions about basic cellular functions, as well as disease states. In the following articles you will discover the multitude of advances investigators have made to accurately measure and quantitate genetic transcripts within the cell.
A great deal of research on pathway analysis is currently focusing on RNA rather than proteins, and the complex RNA networks that regulate gene expression. With the realization that more than 90% of the genome that is transcribed into RNA is not translated into protein, and the growing numbers of naturally occurring microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) being identified and characterized, the important role these RNAs play in normal biological processes and across human diseases is becoming increasingly clear.
The Gene-Expression Undergrowth Have Been Well Trodden, but RNA Paths Want Wear, Too
Hepatitis C virus depends on a functional interaction between its genome and miR-122 for viral stability and replication. Researchers recently used an antisense oligonucleotide that targets the liver-specific microRNA miR-122, blocking its function. [Bluebay2014/Fotolia]
A great deal of research on pathway analysis is currently focusing on RNA rather than proteins, and the complex RNA networks that regulate gene expression.
With the realization that more than 90% of the genome that is transcribed into RNA is not translated into protein, and the growing numbers of naturally occurring microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) being identified and characterized, the important role these RNAs play in normal biological processes and across human diseases is becoming increasingly clear.
This knowledge—combined with the available technology and strategies to decipher RNA pathways and link alterations in the levels or activity of miRNAs or lncRNAs to gene expression, epigenetic mechanisms, and protein activity in normal and disease phenotypes—is driving the development and clinical testing of novel drug targets and therapeutics that target regulatory RNAs.
For example, a microRNA was targeted in a Phase II clinical study that assessed the effect of miravirsen, an antisense oligonucleotide, in patients with hepatitis C. The study, which was described in 2013 in the New England Journal of Medicine, indicated that miravirsen sequesters the liver-specific microRNA miR-122 in a highly stable heteroduplex, thereby inhibiting its function.
Hepatitis C virus (HCV) depends on a functional interaction between its genome and miR-122 for viral stability and replication. According to the study, inhibition of miR-122 in HCV-infected patients was associated with decreased levels of HCV RNA that continued beyond the treatment period, without evidence of viral resistance.
The therapeutic potential of regulatory RNAs is also being assessed in other conditions such as cancer. Specifically, miRNAs and other ncRNAs in cancer initiation, progression, and metastasis are being studied by George Calin, M.D., Ph.D., a professor of experimental therapeutics, MD Anderson Cancer Center, University of Texas. Dr. Calin’s group is scouring the “microRNAome” to identify miRNAs of about 21–22 nucleotides that can serve as reliable biomarkers for cancer diagnosis and to guide decision-making in patient management, including as predictors of survival and response to drug therapy.
miRNAs are involved in every aspect of tumorigenesis, cancer progression, and dissemination. Not only are they expressed in tumor cells, they are also stably expressed in exosomes and are present in various bodily fluids, where they can act like hormones and signaling molecules. Comparative profiling of these fluids for differences in miRNA levels between patients with and without cancer could identify relevant biomarkers.
Analyzing RNA Pathways
Using Qiagen’s Ingenuity Pathway Analysis, researchers can analyze relationships between molecules and diseases of interest by modeling how gene expression patterns affect functional outcomes or disease processes.
Dr. Calin and colleagues have described the significance of miRNA signatures obtained in recent studies involving miRNA profiling of human tumors. An overview appeared 2014 in CA: A Cancer Journal for Clinicians (“MicroRNAome genome: a treasure for cancer diagnosis and therapy”). Also, last February, Dr. Calin gave an account of his group’s work at the Molecular Med Tri Conference in San Francisco.
Technology is not holding back advances in the field of RNA pathway analysis according to Dr. Calin. The main bottleneck at present is in the design of prospective studies needed to confirm the predictive value of miRNA-based biomarkers.
Dr. Calin points to two other key challenges that scientists currently face in translating research findings into diagnostic, prognostic, and therapeutic tools. One is the difficulty in selecting an miRNA target, mainly because an individual miRNA could have a role in regulating tens, hundreds, or even thousands of protein-coding genes. For drug discovery, the aim is to identify miRNAs that affect a single pathway of interest to help limit off-target effects. The need for novel delivery systems for RNA-targeted drugs is another key challenge.
At the Molecular Med Tri Conference, Jean-Noel Billaud, Ph.D., principal scientist at Qiagen Bioinformatics, presented a case study demonstrating how the company’s Ingenuity Pathway Analysis technology can be used to conduct a systems biology analysis to identify the pathways, potential upstream regulators, and downstream outcomes involved in the host response to West Nile Virus (WNV) infection. Dr. Billaud also discussed how to interpret the results from a biological perspective.
In his presentation, Dr. Billaud described the first step in this analytical process as the acquisition of RNA sequence data using next-generation sequencing techniques for the purpose of characterizing and quantifying differential gene expression between an infected and uninfected cell. The CLC Cancer Research Workbench tool is used to process the sequence data, and the results are imported directly into the IPA system.
Analysis of differential gene expression aims to answer a series of key questions, including the following: What metabolic and/or signaling pathway(s) is activated or inhibited? Is there an overlap of the genes or pathways that are activated or inhibited? What are the potential upstream, downstream, functional, and phenotypic implications of this pathway activation or inhibition?
Dr. Billaud described other questions researchers might attempt to answer through the use of IPA: What are the identifying the underlying transcriptional programs? Which biological processes are involved and in what way? Are there splice variants of interest? What type of regulation is involved?
In the WNV case study, IPA predicted activation of the interferon signaling pathway and added statistically and functionally relevant biological processes to the WNV-related biochemical network the system developed. IPA is able to simulate the effects of interferon pathway activation on neighboring molecules and processes, which enables broader modeling of antiviral responses, prediction of the effects on viral replication, and identification of upstream transcriptional regulators of antiviral and related anti-inflammatory processes, for example.
These data and analytical capabilities may allow researchers to propose new hypotheses that connect molecules in regulatory networks to disease-related pathways in a predictive way, leading to the identification of a “master regulator” that could serve as a disease-specific drug target, according to Dr. Billaud.
In the WNV example, he described the use of the Molecule Activity Predictor (MAP) function in IPA to test the hypothesis that CLEC7A is a host susceptibility factor required by WNV to stimulate an immune response in the brains of infected patients, contributing to the development of life-threatening encephalitis. The MAP function simulates the inhibition or downregulation of CLEC7A, showing how it would likely reduce the risk of WNV-associated encephalitis. These types of hypotheses would then need to be tested and validated.
Pathways Driving B-Cell Differentiation
Robert C. Rickert, Ph.D., professor and director of the Tumor Microenvironment and Metastasis Program at Sanford-Burnham Medical Research Institute, is using conditional gene targeting to identify the genes and biochemical pathways that play a role at specific stages of B-cell differentiation. With this approach, it is possible to knock out targeted genes in a mouse at different stages of B-cell development, and to do so in an inducible fashion, allowing you “to look at how it affects different signal transduction pathways in a context-specific manner,” says Dr. Rickert.
When applied to a relevant mouse model of disease—such as a B-cell lymphoma—this inducible genetic system should yield effects similar to those that could be obtained with a drug capable of blocking the activity of the targeted gene product. Dr. Rickert and colleagues are exploring the similarity between the effects achieved with conditional gene targeting and those of recently approved drugs to treat chronic lymphocytic leukemia (CLL) and some forms of lymphoma such as idelalisib and ibrutinib, which are both inhibitors of the B-cell receptor pathway via blocking of PI3K or Bruton’s tyrosine kinase (BTK), respectively.
Dr. Rickert presented his group’s latest research at a Keystone Symposium Conference, PI 3-Kinase Signaling Pathways in Disease, which took place last January in Vancouver. In his talk, Dr. Rickert emphasized that the phosphatidyl inositol-3 kinase (PI3K) pathway is a major regulator B lymphocyte differentiation and function.
Dr. Rickert has also applied conditional gene targeting to compare the roles of the NFκB and PI3K pathways in B-cell maturation. He has shown that while both pathways are essential at some stages of B-cell differentiation, only one pathway may be necessary for B-cell maintenance and survival.
“Ultimately we want to gain more insight at the biochemical level into single cells and the heterogeneity of the cell populations we’re interested in,” says Dr. Rickert. Tumors and cancer cell populations are quite heterogeneic, and better biochemical tools are needed to be able to sort through these populations of cells and “look at some of the more interesting, rogue cells, such as cancer stem cells,” he adds.
An Evolutionary Approach
In his laboratory at Hebrew University of Jerusalem, researcher Yuval Tabach, Ph.D., is using computational tools to analyze and compare the genomes and proteins of hundreds of species to identify evolutionary patterns of conservation and loss that point to connections between molecular pathways and disease.
“The main power of this phylogenetic profiling approach is that if you look at proteins across evolution, some are lost at certain points in certain species,” says Dr. Tabach. For example, proteins involved in the tricarboxylic acid (TCA) cycle have been highly conserved across some species, but have disappeared in others because those species have lost their mitochondria.
Dr. Tabach and colleagues have shown that sets of genes associated with particular diseases have similar phylogenetic profiles. They are also using this approach to identify genes associated with longevity, cancer resistance, and various extreme environmental conditions.
Phylogenetic profiling to connect patterns of conservation and loss across millions of years of evolution can be applied to entire proteins, protein domains, and RNA molecules such as microRNAs. The potential applicability of this approach to drug discovery and development is multifaceted.
For example, given a gene known to be related to a certain disease, the ability to identify other genes with a similar phylogenetic profile might reveal genetic factors that could explain incomplete penetrance or the variability of disease severity in different affected individuals. Alternatively, identification of a candidate gene in one patient could serve as the basis for identifying other key factors in other patients with the same disease using the phylogenetic profile.
Compared to strategies such as gene expression analysis or protein-protein interaction mapping for identifying disease-related genes, phylogenetic profiling “is much faster” and will become an increasingly powerful tool as the genome sequences of more species become available, explains Dr. Tabach.
The Israeli start-up company ReThink Pharmaceuticals is using the molecular networks generated through this phylogenetic profiling work for the purpose of drug repositioning. “If you know that a certain drug targets a gene, we can build a network to find other genes/proteins that interact with the drug target,” asserts Dr. Tabach, citing preliminary results that demonstrate the ability to predict additional effects of a drug candidate.
A critical component of RNA interference (RNAi) studies is the validation of gene expression inhibition. RNAi experiments have many sources of variation that make accurate quantitation of target mRNA difficult when qPCR is used. Variation in the potency and stability of short interfering RNA (siRNA), coupled with differences in transfection efficiency and protein turnover, results in varying gene knockdown efficiency.
Over the past 10 years, scientists say new methods, including deep sequencing and DNA tiling arrays, have enabled the identification and characterization of the human transcriptome. These techniques completely changed our understanding of genome organization and content and revealed that a much larger part of the human genome is transcribed into RNA than was previously assumed—about 70%.
Over the past 10 years, scientists say new methods, including deep sequencing and DNA tiling arrays, have enabled the identification and characterization of the human transcriptome. These techniques completely changed our understanding of genome organization and content and revealed that a much larger part of the human genome is transcribed into RNA than was previously assumed—about 70%.
Last year researchers, including Tim Mercer, Ph.D., at the Institute for Molecular Bioscience-University of Queensland, Roche Nimblegen, and John Rinn, Ph.D., and his team in the department of stem cell and regenerative biology at Harvard, reported that “transcriptomic analyses have revealed an ‘unexpected complexity’ to the human transcriptome, the depth and breadth of which exceeds current RNA sequencing capability.”
These scientists used these techniques to identify and characterize unannotated transcripts whose rare or transient expression is below the detection limits of conventional sequencing approaches. The data also show that intermittent sequenced reads observed in conventional RNA sequencing datasets, previously dismissed as noise, are indicative of unassembled rare transcripts. Collectively, they say these results reveal the range, depth, and complexity of a human transcriptome that is far from fully characterized.
Noncoding transcripts are RNA molecules that include classical “housekeeping” RNAs such as transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs), which are constitutively expressed and play critical roles in protein biosynthesis.
Among these noncoding RNAs are numerous long noncoding RNAs (lncRNAs), which are defined as endogenous cellular RNAs of more than 200 nucleotides in length that lack an open reading frame of significant length (less than 100 amino acids). The RNA molecules constitute a heterogeneous group, allowing them, scientists point out, to cover a broad spectrum of molecular and cellular functions by implementing different modes of action. lncRNAs are roughly classified based on their position relative to protein-coding genes as intergenic (between genes), intragenic/intronic (within genes), and antisense. Initial efforts to characterize these molecules demonstrated that they function in cis, regulating their immediate genomic neighbors.
Regulatory Levels
lncRNAs can regulate gene expression at epigenetic, transcriptional, and post-transcriptional levels and take part in various physiological and pathological processes, such as cell development, immunity, oncogenesis, clinical disease processes, and more. A classic lncRNA, HOTAIR, was originally identified through work done by Howard Chang, M.D., Ph.D., at Stanford, and Dr. Rinn. Their research eventually led to the discovery of this 2.2 kilobase spliced RNA transcript that interacts with Polycomb group proteins to modify chromatin and repress transcription of the human HOX genes, which regulate development. It remains unclear as to exactly this is accomplished.
HOTAIR, it was found, originates from the HOXC locus and represses transcription across 40 kb of that locus by altering the chromatin trimethylation state. Hox genes, a highly conserved subgroup of the homeobox superfamily, regulate numerous processes including apoptosis, receptor signaling, differentiation, motility, and angiogenesis. Aberrations in Hox gene expression have been reported in abnormal development and malignancy.
HOTAIR works to repress Hox gene expression by directing the action of Polycomb chromatin remodeling complexes in trans to govern the cells’ epigenetic state and subsequent gene expression.HOTAIR expression is increased in primary breast tumors and metastases and its expression level in primary tumors can predict eventual metastasis and death. The recent discovery that lncRNA HOTAIRcan link chromatin changes to cancer metastasis furthers the relevance of lncRNAs to human disease.
Dr. Chang and his colleagues say that the finding that several lncRNAs can control transcriptional alteration implies that the difference in lncRNA profiling between normal and cancer cells is not merely the secondary effect of cancer transformation, and that lncRNAs are strongly associated with cancer progression. The researchers showed that lncRNAs in the HOX loci become systematically dysregulated during breast cancer progression.
They further demonstrated that enforced expression of HOTAIR in epithelial cancer cells induced genome-wide retargeting of polycomb repressive complex 2 (PRC2) to an occupancy pattern more resembling embryonic fibroblasts, leading to altered histone H3 lysine 27 methylation, gene expression, and increased cancer invasiveness and metastasis in a manner dependent on PRC2.
On the other hand they noted loss of HOTAIR can inhibit cancer invasiveness, particularly in cells that possess excessive PRC2 activity. These findings indicate that lncRNAs have active roles in modulating the cancer epigenome and may be important targets for cancer diagnosis and therapy. Thus, the investigators say, differential expression of lncRNAs may be profiled to aid in cancer diagnosis and prognosis and in the selection of potential therapeutics.
Two years ago the GENCODE consortium, within the framework of the ENCODE project, presented, and analyzed the most complete human lncRNA annotation to date. The data comprise 9,277 manually annotated genes producing 14,880 transcripts. The identification and annotation of this wealth of lncRNAs leaves scientists with a lot of research to do to fully characterize the varied functions of these unusual RNAs. Their identification also challenges technology developers to produce the tools to necessary for these analyses.
Drug-drug interactions (DDIs) are of particular concern for regulatory agencies and the pharmaceutical industry for drug safety. Induction of drug metabolizing enzymes by pharmaceuticals, nutraceuticals, and lifestyle influences is one type of DDI in which the influence of a perpetrator molecule increases the enzyme capacity that can metabolize a victim molecule, rendering it ineffective as a therapy. To evaluate this potential, screening assays have been developed, such as the use…
Biomarkers defining specific phenotypes are becoming increasingly important for developing new drugs for specific patient subpopulations. The value of a new biomarker is measured by its ability to reduce risk. Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by…
Imanova takes a structured approach to the development of imaging biomarkers, or i-biomarkers.
Biomarkers defining specific phenotypes are becoming increasingly important for developing new drugs for specific patient subpopulations. The value of a new biomarker is measured by its ability to reduce risk.
Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by shutting down a development program before it enters Phase II. A meaningful risk-benefit analysis of a biomarker requires estimates of its cost and accuracy, as well as the consequences of decisions that it will enable.
For the biomarker to be of value, the cost of its development has to be less than the projected costs of development from Phase II onwards, discounted to present time. While multiple competing business considerations affect a pharmaceutical company’s decision to proceed with a biomarker program, the skyrocketing market for biomarker discovery underscores the pharmaceutical industry’s hope that biomarkers will bolster the success rates of pipeline products.
“Imaging biomarkers have been Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by shutting down a development program before it enters Phase II. A meaningful risk-benefit analysis of a biomarker requires estimates of its cost and accuracy, as well as the consequences of decisions that it will enable.
Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by shutting down a development program before it enters Phase II. A meaningful risk-benefit analysis of a biomarker requires estimates of its cost and accuracy, as well as the consequences of decisions that it will enable.
For the biomarker to be of value, the cost of its development has to be less than the projected costs of development from Phase II onwards, discounted to present time. While multiple competing business considerations affect a pharmaceutical company’s decision to proceed with a biomarker program, the skyrocketing market for biomarker discovery underscores the pharmaceutical industry’s hope that biomarkers will bolster the success rates of pipeline products.
“Imaging biomarkers have been largely underutilized in drug development,” says Kevin Cox, Ph.D., CEO of London-based Imanova. “But we believe that molecular imaging has the power to assist in successful translation of molecules by reducing the risk of several specific causes of failure in Phase II clinical studies. Imaging biomarkers, or i-biomarkers, are especially valuable in giving confidence of tissue delivery, determination of target engagement, and the evaluation of a drug’s pharmacodynamic effects.”
While imaging is routinely used in clinical diagnostics for cancer, its acceptance in drug development has been slow. “This is a highly specialized area of knowledge,” Dr. Cox observes. “Designing imaging experiments to answer the right questions is not trivial. Combined with the perceived high costs and dearth of well-equipped facilities, this has slowed down the adoption of imaging as an integral step in drug development.”
Imanova presents an innovative and highly integrated solution in reducing the barriers for use of molecular imaging. Located in the former GlaxoSmithKline imaging center, Imanova’s staff applies the knowledge needed for translational application of imaging science.
“Another historical barrier for use of molecular imaging has been the lack of versatile PET tracers for key therapeutic targets,” remarks Dr. Cox. Together with its pharmaceutical clients, Imanova develops proprietary tracers that can answer critical questions about target engagement directly after drug administration. A structured approach for i-biomarker development takes the novel tracer from the candidate pool to clinical validation.
Uniquely, Imanova utilizes in silico biomathematical modeling to predict a candidate with ideal physicohemical characteristics. “The i-biomarker development pipeline adheres to a strict quality system,” continues Dr. Cox. “We not only provide candidate selection and labeling, but also rigorous preclinical evaluation in several species, combined with blood chemistry or other physiological measurements.”
The resulting biomarker provides quantitative information to make informed go/no-go decisions. Imanova hopes to develop an open innovation approach to i-biomarker research, and to encourage pharmaceutical companies to collaborate on tracer development.
“By collaborating in this pre-competitive space, a pharma-academic consortium can de-risk i-biomarker development programs and generate new tools to eliminate costs associated with futile activities downstream,” concludes Dr. Cox. “Most tracers need to be utilized early in the drug development process. Used at the right time, imaging biomarkers are able to inform the design of Phase II studies, including dose ranging and possibly patient selection, saving many months in development and millions of dollars in costs.”
Answers from Big Data
“Clinical bioinformatics is the application of a data-driven, high-tech approach in clinical setting,” says Jerome Wojcik, Ph.D., CEO of Quartz Bio, a clinical bioinformatics service provider located in Plan-Les-Ouates, Switzerland. “We use clinical bioinformatics to adapt treatment to patients, that is, to identify cohorts that respond to the drug in a predictable manner,” says Dr. Wojcik.
Pharmaceutical partners supply Quartz Bio with data collected in a course of clinical trials. The data (which may include information from protein and RNA expression, genotyping, molecular diagnostics, and flow cytometry studies) often exists in silos within a pharma company. To make sense of the data, Quartz Bio integrates heterogeneously formatted data, analyzes it for consistency, and identifies gaps and outliers.
Dr. Wojcik’s team dedicates over 40% of the overall analysis time to the biomarker data management. This key step is crucial for the quality of the overall analysis. According to Quartz Bio, all the data-management processes are documented, auditable, and reproducible.
Once the “Big Data” horde is adequately cleaned up, the team applies adaptive statistical methods to generate multiple hypotheses linking the drug action with subpopulations of patients. “Our challenge is to generate reliable hypotheses on a fairly small statistical patient sample, for example, a thousand patients, but using millions of biomarker datapoints,” continues Dr. Wojcik. “We do not rely on statistics alone. Graphical visualization adapted to the objectives of the study is necessary for interpretation of results.”
In a recent project, Quartz Bio analyzed multiple oncology biomarkers, such as gene expression, circulating tumor cells, and immunohistochemistry, to identify patient cohorts that would most likely benefit from a novel treatment. Biomarker analysis revealed a subpopulation whose survival rate increased significantly over the population average, bringing a potential application of personalized medicine closer to reality.
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