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Crystal Resolution in Raman Spetctoscopy for Pharmaceutical Analysis

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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

By Geoffrey P.S. Smith, Gregory S. Huff, Keith C. Gordon

Spectroscopy  Feb 01, 2016; 31(2): 42–50      http://www.spectroscopyonline.com/investigating-crystallinity-using-low-frequency-raman-spectroscopy-applications-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).

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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).

 

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Figure 2: Comparison of the low-frequency regions of amorphous and crystalline solids.

 

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Figure 3: Raman spectra of griseofulvin

Ensuring Accuracy

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.

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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,T_g = 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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 Group Wavenumbers and an Introduction to the Spectroscopy of Benzene Rings

By Brian C. Smith

Spectroscopy Mar 01, 2016; 31(3): 34–37   http://www.spectroscopyonline.com/group-wavenumbers-and-introduction-spectroscopy-benzene-rings

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.

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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.

http://images.alfresco.advanstar.com/alfresco_images/pharma/2016/02/12/645ee751-2432-4444-8af1-ded62697ee27/IR-Spectral-figure02_web.jpg

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.

The IR Spectrum of Benzene

The IR spectrum of benzene is shown in Figure 3.

http://images.alfresco.advanstar.com/alfresco_images/pharma/2016/02/12/645ee751-2432-4444-8af1-ded62697ee27/IR-Spectral-figure03_web.jpg

Figure 3: The IR spectrum of benzene, measured as a capillary thin film between two KBr windows.

……

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.

References

1. B.C. Smith, Spectroscopy 30(4), 18–23 (2015).
2. B.C. Smith, Infrared Spectral Interpretation: A Systematic Approach(CRC Press, Boca Raton, Florida, 1995).
3. R. Morrison and R. Boyd, Organic Chemistry, 7th Edition (Allyn and Bacon, New York, New York, 2003).
4. B.C. Smith, Spectroscopy30(9), 40–45 (2015).

 

Bioimaging: From Cells To Molecules 2016 Agenda

http://selectbiosciences.com/mobile/conferences/agenda.aspx?pid=4332&conf=BC2016

09:00	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
11:45	Democratising Live-Cell High-Speed Super-Resolution Microscopy
12:15	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
17:00	Close of Conference

 

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Human Factor Engineering: New Regulations Impact Drug Delivery, Device Design And Human Interaction

Posted in 3D Printing for Medical Application, Advanced Drug Manufacturing Technology, Bio-MEMS, Bioengineering & reverse engineering design, BioPrinting in Regenerative Medicine, Cell Level, Commercialization, Components and IRB related issues, Drug Development using MultiOrgan Chip, FDA, FDA Regulatory Affairs, Health Law & Patient Safety, ISO 10993 for Product Registration: FDA & CE Mark for Development of Medical Devices and Diagnostics, Medical Devices R&D Investment, Patient Experience, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, tagged drug delivery system, ergonomics, FDA, FDA compliance, fda regulation, human factor engineering, Medical device, Transdermal Drug Delivery System on February 17, 2016| Leave a Comment »

Human Factor Engineering: New Regulations Impact Drug Delivery, Device Design And Human Interaction

Curator: Stephen J. Williams, Ph.D.

Institute of Medicine report brought medical errors to the forefront of healthcare and the American public (Kohn, Corrigan, & Donaldson, 1999) and  estimated that between

44,000 and 98,000 Americans die each year as a result of medical errors

An obstetric nurse connects a bag of pain medication intended for an epidural catheter to the mother’s intravenous (IV) line, resulting in a fatal cardiac arrest. Newborns in a neonatal intensive care unit are given full-dose heparin instead of low-dose flushes, leading to threedeaths from intracranial bleeding. An elderly man experiences cardiac arrest while hospitalized, but when the code blue team arrives, they are unable to administer a potentially life-saving shock because the defibrillator pads and the defibrillator itself cannot be physically connected.

Human factors engineering is the discipline that attempts to identify and address these issues. It is the discipline that takes into account human strengths and limitations in the design of interactive systems that involve people, tools and technology, and work environments to ensure safety, effectiveness, and ease of use.

 

FDA says drug delivery devices need human factors validation testing

February 9, 2016 | By Varun Saxena from FierceDrugDelivery

Several drug delivery devices are on a draft list of med tech that will be subject to a final guidance calling for the application of human factors and usability engineering to medical devices. The guidance calls called for validation testing of devices, to be collected through interviews, observation, knowledge testing, and in some cases, usability testing of a device under actual conditions of use. The drug delivery devices on the list include anesthesia machines, autoinjectors, dialysis systems, infusion pumps (including implanted ones), hemodialysis systems, insulin pumps and negative pressure wound therapy devices intended for home use. Studieshave consistently shown that patients struggle to properly use drug delivery devices such as autoinjectors, which are becoming increasingly prevalent due to the rise of self-administered injectable biologics. The trend toward home healthcare is another driver of usability issues on the patient side, while professionals sometimes struggle with unclear interfaces or instructions for use.

 

Human–factors engineering, also called ergonomics, or human engineering, science dealing with the application of information on physical and psychological characteristics to the design of devices and systems for human use. ( for more detail see source@ Britannica.com)

The term human-factors engineering is used to designate equally a body of knowledge, a process, and a profession. As a body of knowledge, human-factors engineering is a collection of data and principles about human characteristics, capabilities, and limitations in relation to machines, jobs, and environments. As a process, it refers to the design of machines, machine systems, work methods, and environments to take into account the safety, comfort, and productiveness of human users and operators. As a profession, human-factors engineering includes a range of scientists and engineers from several disciplines that are concerned with individuals and small groups at work.

The terms human-factors engineering and human engineering are used interchangeably on the North American continent. In Europe, Japan, and most of the rest of the world the prevalent term is ergonomics, a word made up of the Greek words, ergon, meaning “work,” and nomos, meaning “law.” Despite minor differences in emphasis, the terms human-factors engineering and ergonomics may be considered synonymous. Human factors and human engineering were used in the 1920s and ’30s to refer to problems of human relations in industry, an older connotation that has gradually dropped out of use. Some small specialized groups prefer such labels as bioastronautics, biodynamics, bioengineering, and manned-systems technology; these represent special emphases whose differences are much smaller than the similarities in their aims and goals.

The data and principles of human-factors engineering are concerned with human performance, behaviour, and training in man-machine systems; the design and development of man-machine systems; and systems-related biological or medical research. Because of its broad scope, human-factors engineering draws upon parts of such social or physiological sciences as anatomy, anthropometry, applied physiology, environmental medicine, psychology, sociology, and toxicology, as well as parts of engineering, industrial design, and operations research.

source@ Britannica.com

The human-factors approach to design

Two general premises characterize the approach of the human-factors engineer in practical design work. The first is that the engineer must solve the problems of integrating humans into machine systems by rigorous scientific methods and not rely on logic, intuition, or common sense. In the past the typical engineer tended either to ignore the complex and unpredictable nature of human behaviour or to deal with it summarily with educated guesses. Human-factors engineers have tried to show that with appropriate techniques it is possible to identify man-machine mismatches and that it is usually possible to find workable solutions to these mismatches through the use of methods developed in the behavioral sciences.

The second important premise of the human-factors approach is that, typically, design decisions cannot be made without a great deal of trial and error. There are only a few thousand human-factors engineers out of the thousands of thousands of engineers in the world who are designing novel machines, machine systems, and environments much faster than behavioral scientists can accumulate data on how humans will respond to them. More problems, therefore, are created than there are ready answers for them, and the human-factors specialist is almost invariably forced to resort to trying things out with various degrees of rigour to find solutions. Thus, while human-factors engineering aims at substituting scientific method for guesswork, its specific techniques are usually empirical rather than theoretical.

 

 

 

 

 

 

 

 

 

 

 

The Man-Machine Model: Human-factors engineers regard humans as an element in systems

The simple man-machine model provides a convenient way for organizing some of the major concerns of human engineering: the selection and design of machine displays and controls; the layout and design of workplaces; design for maintainability; and the work environment.

Components of the Man-Machine Model

1. human operator first has to sense what is referred to as a machine display, a signal that tells him something about the condition or the functioning of the machine
2. Having sensed the display, the operator interprets it, perhaps performs some computation, and reaches a decision. In so doing, the worker may use a number of human abilities, Psychologists commonly refer to these activities as higher mental functions; human-factors engineers generally refer to them as information processing.
3. Having reached a decision, the human operator normally takes some action. This action is usually exercised on some kind of a control—a pushbutton, lever, crank, pedal, switch, or handle.
4. action upon one or more of these controls exerts an influence on the machine and on its output, which in turn changes the display, so that the cycle is continuously repeated

 

Driving an automobile is a familiar example of a simple man-machine system. In driving, the operator receives inputs from outside the vehicle (sounds and visual cues from traffic, obstructions, and signals) and from displays inside the vehicle (such as the speedometer, fuel indicator, and temperature gauge). The driver continually evaluates this information, decides on courses of action, and translates those decisions into actions upon the vehicle’s controls—principally the accelerator, steering wheel, and brake. Finally, the driver is influenced by such environmental factors as noise, fumes, and temperature.

 

How BD Uses Human Factors to Design Drug-Delivery Systems

Posted in Design Services by Jamie Hartford on August 30, 2013

 Human factors testing has been vital to the success of the company’s BD Physioject Disposable Autoinjector.

The BD Physioject Disposable Autoinjector offers users a 360° view of the drug injection process and features a one-touch injection button.

Improving the administration and compliance of drug delivery is a common lifecycle strategy employed to enhance short- and long-term product adoption in the biotechnology and pharmaceutical industries. With increased competition in the industry and heightened regulatory requirements for end-user safety, significant advances in product improvements have been achieved in the injectable market, for both healthcare professionals and patients. Injection devices that facilitate preparation, ease administration, and ensure safety are increasingly prevalent in the marketplace.

Traditionally, human factors engineering addresses individualized aspects of development for each self-injection device, including the following:

• Task analysis and design.
• Device evaluation and usability.
• Patient acceptance, compliance, and concurrence.
• Anticipated training and education requirements.
• System resilience and failure.

To achieve this, human factors scientists and engineers study the disease, patient, and desired outcome across multiple domains, including cognitive and organizational psychology, industrial and systems engineering, human performance, and economic theory—including formative usability testing that starts with the exploratory stage of the device and continues through all stages of conceptual design. Validation testing performed with real users is conducted as the final stage of the process.

To design the BD Physioject Disposable Autoinjector System , BD conducted multiple human factors studies and clinical studies to assess all aspects of performance safety, efficiency, patient acceptance, and ease of use, including pain perception compared with prefilled syringes.5 The studies provided essential insights regarding the overall user-product interface and highlighted that patients had a strong and positive response to both the product design and the user experience.

As a result of human factors testing, the BD Physioject Disposable Autoinjector System provides multiple features designed to aide in patient safety and ease of use, allowing the patient to control the start of the injection once the autoinjector is placed on the skin and the cap is removed. Specific design features included in the BD Physioject Disposable Autoinjector System include the following:

• Ergonomic design that is easy to handle and use, especially in patients with limited dexterity.
• A 360° view of the drug and injection process, allowing the patient to confirm full dose delivery.
• A simple, one-touch injection button for activation.
• A hidden needle before and during injection to reduce needle-stick anxiety.
• A protected needle before and after injection to reduce the risk of needle stick injury.

 

YouTube VIDEO: Integrating Human Factors Engineering (HFE) into Drug Delivery

 

Notes:

 

 

The following is a slideshare presentation on Parental Drug Delivery Issues in the Future

 The Dangers of Medical Devices

The FDA receives on average 100,000 medical device incident reports per year, and more than a third involve user error.

In an FDA recall study, 44% of medical device recalls are due to design problems, and user error is often linked to the poor design of a product.

Drug developers need to take safe drug dosage into consideration, and this consideration requires the application of thorough processes for Risk Management and Human Factors Engineering (HFE).

Although unintended, medical devices can sometimes harm patients or the people administering the healthcare. The potential harm arises from two main sources:

1. failure of the device and
2. actions of the user or user-related errors. A number of factors can lead to these user-induced errors, including medical devices are often used under stressful conditions and users may think differently than the device designer.

Human Factors: Identifying the Root Causes of Use Errors

Instead of blaming test participants for use errors, look more carefully at your device’s design.

Great posting on reasons typical design flaws creep up in medical devices and where a company should integrate fixes in product design.
Posted in Design Services by Jamie Hartford on July 8, 2013

 

 

YouTube VIDEO: Integrating Human Factors Engineering into Medical Devices

 

 

Notes:

 

 Regulatory Considerations

• Unlike other medication dosage forms, combination products require user interaction
•  Combination products are unique in that their safety profile and product efficacy depends on user interaction

Human Factors Review: FDA Outlines Highest Priority Devices

Posted 02 February 2016By Zachary Brennan on http://www.raps.org/Regulatory-Focus/News/2016/02/02/24233/Human-Factors-Review-FDA-Outlines-Highest-Priority-Devices/ 

The US Food and Drug Administration (FDA) on Tuesday released new draft guidance to inform medical device manufacturers which device types should have human factors data included in premarket submissions, as well final guidance from 2011 on applying human factors and usability engineering to medical devices.

FDA said it believes these device types have “clear potential for serious harm resulting from use error and that review of human factors data in premarket submissions will help FDA evaluate the safety and effectiveness and substantial equivalence of these devices.”

Manufacturers should provide FDA with a report that summarizes the human factors or usability engineering processes they have followed, including any preliminary analyses and evaluations and human factors validation testing, results and conclusions, FDA says.

The list was based on knowledge obtained through Medical Device Reporting (MDRs) and recall data, and includes:

• Ablation generators (associated with ablation systems, e.g., LPB, OAD, OAE, OCM, OCL)
• Anesthesia machines (e.g., BSZ)
• Artificial pancreas systems (e.g., OZO, OZP, OZQ)
• Auto injectors (when CDRH is lead Center; e.g., KZE, KZH, NSC )
• Automated external defibrillators
• Duodenoscopes (on the reprocessing; e.g., FDT) with elevator channels
• Gastroenterology-urology endoscopic ultrasound systems (on the reprocessing; e.g., ODG) with elevator channels
• Hemodialysis and peritoneal dialysis systems (e.g., FKP, FKT, FKX, KDI, KPF ODX, ONW)
• Implanted infusion pumps (e.g., LKK, MDY)
• Infusion pumps (e.g., FRN, LZH, MEA, MRZ )
• Insulin delivery systems (e.g., LZG, OPP)
• Negative-pressure wound therapy (e.g., OKO, OMP) intended for home use
• Robotic catheter manipulation systems (e.g., DXX)
• Robotic surgery devices (e.g., NAY)
• Ventilators (e.g., CBK, NOU, ONZ)
• Ventricular assist devices (e.g., DSQ, PCK)

Final Guidance

In addition to the draft list, FDA finalized guidance from 2011 on applying human factors and usability engineering to medical devices.

The agency said it received over 600 comments on the draft guidance, which deals mostly with design and user interface, “which were generally supportive of the draft guidance document, but requested clarification in a number of areas. The most frequent types of comments requested revisions to the language or structure of the document, or clarification on risk mitigation and human factors testing methods, user populations for testing, training of test participants, determining the appropriate sample size in human factors testing, reporting of testing results in premarket submissions, and collecting human factors data as part of a clinical study.”

In response to these comments, FDA said it revised the guidance, which supersedes guidance from 2000 entitled “Medical Device Use-Safety: Incorporating Human Factors Engineering into Risk Management,” to clarify “the points identified and restructured the information for better readability and comprehension.”

Details

The goal of the guidance, according to FDA, is to ensure that the device user interface has been designed such that use errors that occur during use of the device that could cause harm or degrade medical treatment are either eliminated or reduced to the extent possible.

FDA said the most effective strategies to employ during device design to reduce or eliminate use-related hazards involve modifications to the device user interface, which should be logical and intuitive.

In its conclusion, FDA also outlined the ways that device manufacturers were able to save money through the use of human factors engineering (HFE) and usability engineering (UE).

– See more at: http://www.raps.org/Regulatory-Focus/News/2016/02/02/24233/Human-Factors-Review-FDA-Outlines-Highest-Priority-Devices/#sthash.cDTr9INl.dpuf

 

Please see an FDA PowerPoint on Human Factors Regulatory Issues for Combination Drug/Device Products here: MFStory_RAPS 2011 – HF of ComboProds_v4

 

 

 

 

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newly developed oxazolidinone antibiotics

Posted in Bacterial Resistance, Clinical & Translational, Commercialization, Curation, Disease Biology, Small Molecules in Development of Therapeutic Drugs, FDA Regulatory Affairs, Glycobiology: Biopharmaceutical Production, Infectious Disease & New Antibiotic Targets, Microbiology, Pharmaceutical Analytics, Pharmaceutical Industry Competitive Intelligence, tagged Antibacterial, gram positive bacteria, organic synthesis, oxazolidinones on November 27, 2015| Leave a Comment »

newly developed oxazolidinone antibiotics

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

 

New Antibacterial oxazolidinones in pipeline by Wockhardt

by DR ANTHONY MELVIN CRASTO Ph.D

 

WCK ?

(5S)-N-{3-[3,5-difluoro-4-(4-hydroxy-4-methoxymethyl-piperidin-1-yl)-phenyl]-2-oxo-oxazolidin-5-ylmethyl}-acetamide

MF C₁₉ H₂₅ F₂ N₃ O_{5, MW 413.42}

Acetamide, N-​[[(5S)​-​3-​[3,​5-​difluoro-​4-​[4-​hydroxy-​4-​(methoxymethyl)​-​1-​piperidinyl]​phenyl]​-​2-​oxo-​5-​oxazolidinyl]​methyl]​-

CAS 957796-51-9

Antibacterial oxazolidinones

THIS MAY BE WCK 4086?????

PATENT

WO 2015173664, US8217058, WO 2012059823, 

 

Oxazolidinone represent a novel chemical class of synthetic antimicrobial agents.Linezolid represents the first member of this class to be used clinically. Oxazolidinones display activity against important Gram-positive human and veterinary pathogens including Methicillin-Resistant Staphylococcus aureus (MRSA), Vancomycin Resistant Enterococci (VRE) and β-lactam Resistant Streptococcus pneumoniae (PRSP). The oxazolidinones also show activity against Gram-negative aerobic bacteria, Gram-positive and Gram-negative anaerobes. (Diekema D J et al., Lancet 2001 ; 358: 1975-82).

Various oxazolidinones and their methods of preparation are disclosed in the literature. International Publication No. WO 1995/25106 discloses substituted piperidino phenyloxazolidinones and International Publication No. WO 1996/13502 discloses phenyloxazolidinones having a multisubstituted azetidinyl or pyrrolidinyl moiety. US Patent Publication No. 2004/0063954, International Publication Nos. WO 2004/007489 and WO 2004/007488 disclose piperidinyl phenyl oxazolidinones for antimicrobial use.

Pyrrolidinyl/piperidinyl phenyl oxazohdinone antibacterial agents are also described in Kim H Y et al., Bioorg. & Med. Chem. Lett., (2003), 13:2227-2230. International Publication No. WO 1996/35691 discloses spirocyclic and bicyclic diazinyl and carbazinyl oxazolidinone derivatives. Diazepeno phenyloxazolidinone derivatives are disclosed in the International Publication No. WO 1999/24428. International Publication No. WO 2002/06278 discloses substituted aminopiperidino phenyloxazolidinone derivatives.

Various other methods of preparation of oxazolidinones are reported in US Patent No. 7087784, US Patent No. 6740754, US Patent No. 4948801 , US Patent No. 3654298, US Patent No. 5837870, Canadian Patent No. 681830, J. Med. Chem., 32, 1673 (1989), Tetrahedron, 45, 1323 (1989), J. Med. Chem., 33, 2569 (1990), Tetrahedron Letters, 37, 7937-40 (1996) and Organic Process Research and Development, 11 , 739-741(2007).

Indian Patent Application No. 2534/MUM/2007 discloses a process for the preparation of substituted piperidino phenyloxazolidinones. International Publication No. WO2012/059823 further discloses the process for the preparation of phosphoric acid mono-(L-{4-[(5)-5-(acetylaminomethyl)-2-oxo-oxazolidin-3-yl]-2,6-difluorophenyl}4-methoxymethyl piperidine-4-yl)ester.

US Patent No. 8217058 discloses (5S)-N-{3-[3,5-difluoro-4-(4-hydroxy-4-methoxymethyl-piperidin-l-yl)-phenyl]-2-oxo-oxazolidin-5-ylmethyl}-acetamide as an antibacterial agent and its process for preparation.

PATENT

WO2015173664

https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2015173664&recNum=1&maxRec=&office=&prevFilter=&sortOption=&queryString=&tab=PCTDescription

 

 

PATENT

http://www.google.st/patents/WO2007132314A2?cl=en

 

Wockhardt Ltd,

(3) (4)

 

PATENT

WO 2012059823

http://www.google.co.in/patents/WO2012059823A1?cl=en

Phosphoric acid mono-(l-{4-[(S)-5-(acetylamino- methyl)-2-oxo-oxazolidin-3-yl]-2,6-difluorophenyl}-4-methoxymethyl-piperidin-4-yl) ester of Formula (A),

the process comprising the steps of:

a) Converting intermediate of Formula (1) into intermediate of Formula (3)

b) Converting intermediate of Formula (3) into intermediate of Formula (5)

c) Converting intermediate of Formula (5) into intermediate of structure (6)

(5) <⁶> d) Converting intermediate of Formula (6) into intermediate of Formula (10)

e) Converting intermediate of Formula (10) into intermediate of Formula (11),

f) Converting intermediate of Formula (11) into compound of Formula (A) or Pharmaceutically acceptable salts thereof

 

 

 

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pathway and network analysis of complex ‘omics data

Posted in Clinical Diagnostics, Clinical Genomics, Commercialization, Computational Biology/Systems and Bioinformatics, Curation, Genetics & Pharmaceutical, Genome Biology, Health Economics and Outcomes Research, Health Law & Patient Safety, Innovations, Intellectual Property, Innovations, Commercialization, Investment in technological breakthrough, Law and Medicine Conflicts, Metabolomics, Molecular Genetics & Pharmaceutical, Patents, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Analytics, Population Health Management, Genetics & Pharmaceutical, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, RNA Biology, Cancer and Therapeutics, tagged discrepancy between the FDA and the manufacturer, Identify causal variants from human sequencing data, pathway and network analysis, Pathway Genomics on October 23, 2015| Leave a Comment »

pathway and network analysis of complex ‘omics data

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

While blood tests can be used to detect some cancers, the FDA said a San Diego company has no proof its blood test works in patients who have not already been diagnosed with some form of the disease.

WASHINGTON, Sept. 25 (UPI) — A San Diego company selling an early cancer detection test was notified by the U.S. Food and Drug Administration it can find no evidence the test actually works, and is concerned it could prove to be harmful for some people.

Pathway Genomics debuted its CancerIntercept test in early September with claims it can detect cancer cell DNA in the blood, picking up mutations linked to as many as 10 different cancers. The goal is to catch cancer early in people who are “otherwise healthy” and not showing symptoms of the disease.

“Based on our review of your promotional materials and the research publication cited above, we believe you are offering a high risk test that has not received adequate clinical validation and may harm the public health,” said FDA Deputy Director James L. Woods in a letter to the company.

CancerIntercept is billed by the company as a blood test looking for DNA fragments in the bloodstream and testing them for 96 genomic markers it says are found in several specific tumor types.

The direct-to-consumer test can be purchased through the Pathway Genomics website, with programs ranging from a one-time test to a quarterly “subscription” for people who want regular testing.

The company states, in several sections of its website, “the presence of one or more of these genomic markers in a patient’s bloodstream may indicate that the patient has a previously undetected cancer. However, the test is not diagnostic, and thus, follow-up screening and clinical testing would be required to confirm the presence or absence of a specific cancer in the patient.”

The FDA is concerned that people may seek treatment for tumors that do not require medical attention, or spend money and possibly seek out treatment they do not need at all — in either case, unnecessary treatment for cancer is potentially harmful to people, the agency said.

CancerIntercept has not been approved by the FDA for use as a medical device, nor has it been subjected to peer review as most tests of its type would be. The company published a white paper on its website which outlines how the test works, supporting its efficacy with references to several clinical trials on detection of mutated DNA in the bloodstream.

Glenn Braunstein, Chief Medical Officer at Pathway Genomics, told The VergePathway had validated its tests with “hundreds” of patients, though those patients had well-defined, often advanced cancers.

In the letter from the FDA, Woods requests the company provide a timeline for meeting with the agency to review plans for future longitudinal studies on the product and specific details on studies that have been conducted before it was made available to consumers.

http://www.upi.com/Health_News/2015/09/25/FDA-Start-ups-cancer-blood-test-may-be-harmful/4191443181676/

The clinical laboratory is an essential player in the treatment of cancer providing a diagnostic, potentially a prognostic, and follow-up treatment armamentarium.  The laboratory diagnostics industry has grown over the last half century into  a highly accurate, well regulated industry with highly automated and point of care technologies.  Prior to introduction, the tests that are put on the market have to be validated prior to introduction.

How are they validated?

The most common approach is for the test to be used concomitantly with treatment in a clinical trial. Measurements may be made prior to surgical biopsy and treatment, and at a month or 6 months to a year later.  The pharmaceutical and diagnostics industries are independent, even though a large company may have both pharmaceutical and diagnostic divisions.  Consequently, the integration of diagnostics and therapeutics occurs on the front lines of patient care.

How this discrepancy between the FDA and the manufacturer could occur is not clear because prior to introduction, the test would have to be rigorously reviewed by the American Association for Clinical Chemistry, the largest and most competent organization to cover the scientific work, having industry-based committees.  The only problem is that the companies may have products that are patented and have competing claims or interests. This is perhaps most likely to be problematic in the competitive environment of  genomics testing.

The company here reported on is Pathway Genomics, that offers Ingenuity for pathway and variant analysis.  There is no concern about the analysis methods, that are well studied.  The concern is the validation of such method for screening of patients without prior diagnosis.

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This appears to be the new rocky road to verification for validity in diagnostic and treatment application.

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