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Companies Actively Engaging Artificial Intellegence for Drug Design: 2026

Posted in Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Medicine - Applications in Therapeutics, Deep Learning, Drug Development Process, Machine Learning, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, tagged , , , , , , on July 31, 2026| Leave a Comment »

Companies Actively Engaging Artificial Intellegence for Drug Design: 2026

Curator: Stephen J. Williams, Ph.D.

Work-in-Progress

This is not an exhaustive list of all AI companies involved in the drug discovery process.  Some like Chai Discovery will be featured in separate posts and this will be constantly updated as companies are acquired and formed. 

I want to start with a Great interview and white paper by McKinsey where they interview key opinion leaders on the business intelligence on AI firms in the drug discovery world and what to look for in companies that are in this space.

Source: https://www.mckinsey.com/industries/life-sciences/our-insights/how-ai-could-revolutionize-drug-discovery

How AI could revolutionize drug discovery
 | Video
By Alex Devereson, Christoph Sandler, and Lydia The
Watch VIDEO
Artificial intelligence could help scientists develop better medicines faster—and thus improve millions of people’s lives. But for that to happen, companies will need to change the way they work.

Below is a Mckinsey report on How AI will Change the Future of Biotech

https://www.mckinsey.com/featured-insights/the-next-normal/biotech

CHARTING THE FUTURE

AI will be embedded into everyday research

The AI-driven drug discovery industry continues to grow, fueled by new entrants in the market, significant capital investment, and technology maturation. We’ve identified more than 250 companies working in the industry. More than half of them are based in the United States, but key hubs are emerging in Western Europe and Southeast Asia as well. The best of these companies will fully integrate AI into research workflows, as the exhibit shows. By putting AI at the center of the research engine, companies can transform research at scale—and bring about dramatic improvements in patient outcomes. For the full article, see:

Parts of a high-throughput screening (HTS) process embedded with AI technology

1. High-throughput screen commenced with diverse compound sets Scientist selects diverse compound sets (a set of chemical compounds with a wide range of chemical structures) as first high-throughput screen

  • In silico/ on-the-chip simulations
  • In-vitro/ ‘wet lab’ experiments

2. Automated compound selection and transfer Using HTS machinery, individual compounds are transferred to individual wells of cells under experimental conditions

3. Computer-vision-based hit selection Cell response to each compound is measured using microscope analysis (eg, through computer vision techniques); promising compounds are labeled “hits”

4. Automated machine learning (ML) model training from screen outcomes Information from HTS for first few plates is automatically transferred into an ML pipeline, which “learns” how cells respond to each kind of chemical structure

5. Compound library inferencing and prioritization ML algorithm then scans the remainder of the library compounds and predicts which plates should be prioritized to identify the highest number of hits in the next screen

6. Automated compound selection based on ML recommendations ML recommendations are automatically queued and used in the next round of HTS. The cycle continues, with the algorithm continuously learningfrom “real world” outputs. Recommendations trigger scientists to explore new chemical space and begin downstream screening processes more quickly. These recommendations feed into the selection of chemical compounds in step 1

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Human bodies are incredibly complex. It takes many years to discover even just one new medicine to successfully treat a disease. Could artificial intelligence help speed up that process? McKinsey experts believe so. (The following transcript has been edited for clarity.)

Video

How AI could revolutionize drug discovery

Faster and better

 

How relevant and useful is this article for you?

Lydia The: What excites me about AI and drug discovery is the convergence between technology, drug development, and biology, which is going to lead to better drugs being developed faster—using all of the capabilities that Silicon Valley and the tech ecosystem have developed—to help us have even greater impact on patients.

Christoph Sandler: Today, to discover and develop a drug takes more than ten years.

Alex Devereson: We might be able to have drugs in one-tenth of the time, from being discovered to being able to treat patients. Today, many diseases simply have no treatments whatsoever. I think, and I hope, we’re going to see a world where we can generate therapies that can treat those patients very effectively. Fundamentally, we will have life-changing, game-changing drugs—on a scale and at a pace that we’ve never seen before—getting to the right patient at the right time.

The promise of personalized medicine

Christoph Sandler: In the not-so-distant future, we might collect health data across different inputs: from wearables, from our electronic medical records, or from clinical or academic research. And we will have the opportunity, on a voluntary basis, to upload these data into a central, secure, trusted data storage system.

Lydia The: You could imagine using the data to figure out not just what drug might work for you but exactly what drug would work for you at what time, in what sequence, in what dose—really personalized to you.

Will AI replace scientists?

Lydia The: What we’ve found is that technology doesn’t supplant the people. Rather, it will enable scientists to do things faster and better—and potentially develop insights that humans would not be able to develop at all.

Alex Devereson: Scientists will be able to discover things with machine learning that they could never have thought of by themselves, generate entirely new ideas, and move at a pace at which one person can do what it would have taken 100 to do before.

Lydia The: While in previous generations, scientists would spend a lot of their time—maybe even the majority of their time—on manual efforts such as pipetting from one tray to another tray or manually curating and cleaning data, I think AI will help us do all of those things in a more automated and quick way, and develop hypotheses that can then lead a scientist to think through, “What would the next experiment be? What are the implications of the data?”

What companies should do today

Alex Devereson: I think the challenge a lot of companies have is that they want to explore new ideas but, for good reason, they are reluctant to commit until they’ve seen some results and some tangible impact. So they explore a lot of pilots.

Lydia The: We have a term for that. We call it “pilot purgatory”: companies focus on one pilot, and they see the returns in a single pilot, but they don’t establish that approach and way of operating across their organization.

Define a ‘North Star’

Lydia The: One of the most important things for companies to escape pilot purgatory is a real mindset shift, from the top end all the way through the rest of the organization.

Christoph Sandler: It is important for the organization to define what a North Star for them should be, so that the data and analytics transformation of the R&D function can be targeted toward that North Star.

Identify—and solve—the biggest pain points

Alex Devereson: Truly understand what your biggest scientific and operational pain points are. For the lab scientists, for the patients who are in your clinical trials, for the patients who might get the drug: What is the biggest unsolved problem today?

Embed analytics into decision making

Christoph Sandler: Bring the organization on board and help them understand the potential of data and analytics.

Alex Devereson: You need to really, truly redesign a process that embeds analytics, where it’s not something on the side; it’s truly a part of the decision making.

Christoph Sandler: And you need to establish trust in the data and in these models. Equally important as the technical part is the human part.

Deliver value quickly

Alex Devereson: Don’t set up a project that delivers only after five years. Have a view on how you can deliver value in three months—and on what it takes in terms of analytics, data, and technology—with a relentless laser focus on value for the patient and for the scientific process.

 

 

Source: https://www.statnews.com/2026/06/03/alnylam-partner-with-inceptive-nucleics-ai-foundation-models/?utm_campaign=the_readout&utm_medium=email&_hsenc=p2ANqtz-_DOWjLTkMjWQ-ewilam07aNlz79w35ktjIKopXav_x3PpkOIfQIqnnMJlxdznIvjv-lGeo3tI4BUwIuxqEclEu4fHA9Q&_hsmi=422199873&utm_content=422199873&utm_source=hs_email
Biotech Correspondent

The inventor of some of AI’s technical underpinnings is shifting focus to an RNA startup. Also, Rick Pazdur has some ideas on how to determine whether Revolution Medicines’ pancreatic cancer drug can work in a first-line setting. And an oncologist explains why she believes the failed Grail trial matters.

artificial intelligence

The AI architect taking aim at RNAi

Jakob Uszkoreit helped create the transformer architecture — the “T” in ChatGPT — that sparked the generative AI boom. Now he’s trying to do something just as ambitious in drug development, STAT’s Brittany Trang writes. His startup, Inceptive Nucleics, is building biological foundation models that can be applied across a wide range of sequence-based medicines, from RNA interference therapies to mRNA and antisense drugs.

That vision caught the attention of Alnylam, which yesterday announced that it had struck a three-year partnership worth up to $2 billion in potential milestone payments and royalties, along with a $30 million upfront investment. The idea is that AI could perhaps do more than analyze biological data — and instead design the molecules themselves.

 

Top 10 Leading AI Drug Discovery Companies Transforming the Market: Trends, Technologies, Growth Outlook & Competitive Landscape (2026-2034)

IntelMarketResearch

MARKET OVERVIEW:

Artificial Intelligence (AI) is redefining the future of pharmaceutical research, making drug development faster, smarter, and more cost-effective than ever before. What once took more than a decade and billions of dollars can now be significantly accelerated through advanced machine learning algorithms, generative AI, predictive analytics, and computational biology.

AI Drug Discovery Market is projected to grow from USD 3.41 billion in 2026 to USD 10.45 billion by 2034, registering a robust 15.7% CAGR. This remarkable expansion reflects the pharmaceutical industry’s increasing reliance on AI-powered platforms to improve research efficiency, reduce clinical failures, and identify breakthrough therapies for complex diseases.

As healthcare systems worldwide demand faster innovation and precision medicine continues to evolve, AI is becoming an essential component of modern drug discovery rather than an experimental technology.

Key Technologies Powering AI Drug Discovery

The industry’s rapid growth is supported by several advanced technologies that work together throughout the drug development pipeline.

Machine Learning helps identify disease targets, recognize biological patterns, and predict therapeutic outcomes using historical datasets.

Deep Learning improves molecular analysis by recognizing highly complex biological relationships that conventional computational methods may overlook.

Generative AI creates entirely new molecular structures designed for specific therapeutic applications, enabling scientists to explore millions of virtual compounds before physical synthesis.

Predictive Modeling estimates toxicity, efficacy, pharmacokinetics, and safety profiles early in development, reducing unnecessary experimentation.

Molecular Docking Simulations evaluate how drug molecules interact with proteins, helping researchers prioritize candidates with the highest therapeutic potential.

AI-Driven Clinical Trial Optimization assists pharmaceutical companies in selecting suitable patient populations, improving trial design, and increasing the probability of successful outcomes.

Top 10 Leading AI Drug Discovery Companies Driving Innovation

The competitive landscape is rapidly evolving, with established technology providers and specialized biotechnology companies competing to develop next-generation AI platforms.

1. Insilico Medicine

Insilico Medicine has become one of the industry’s most recognized innovators through its end-to-end AI drug discovery platform. The company combines generative AI with biological research to accelerate target identification and therapeutic development.

2. Exscientia

Exscientia focuses on AI-designed precision medicines and has established multiple collaborations with leading pharmaceutical organizations to improve drug candidate selection and optimization. Exscientia is a pioneering artificial intelligence-driven pharmatech company that was acquired by Recursion Pharmaceuticals in November 2024

3. Atomwise

Known for its deep learning platform, Atomwise uses artificial intelligence to identify promising small-molecule therapies across oncology, infectious diseases, and rare disorders.

4. Recursion Pharmaceuticals

Recursion Pharmaceuticals integrates high-content imaging, automation, and AI to analyze millions of biological experiments, enabling large-scale phenotypic drug discovery.

5. BERG

BERG (now operating or known as BPGbio) specializes in biology-driven AI by combining multi-omics datasets with artificial intelligence to identify biomarkers and novel therapeutic opportunities.

6. Cyclica

Cyclica’s AI platform focuses on polypharmacology, helping researchers understand complex drug-protein interactions while improving drug design efficiency. , focusing on MatchMaker and POEM technology platforms before being acquired by Recursion for $40 million in May 2023.

7. GNS Healthcare

GNS Healthcare applies causal AI and patient-level analytics to support personalized medicine and identify drug repurposing opportunities.

8. DeepCure

DeepCure develops generative chemistry platforms capable of designing optimized molecules while reducing early-stage research complexity.

9. Schrödinger

Schrödinger combines computational chemistry, molecular simulation, and AI technologies to improve molecular modeling and accelerate pharmaceutical innovation.

10. IBM Watson Health & DeepMind Health

These technology pioneers continue expanding AI capabilities across healthcare by leveraging advanced analytics, large-scale biological data processing, and intelligent research platforms that support drug development initiatives. https://www.ibm.com/products/watsonx

There are many other companies that are using AI in Innovative ways to support drug discovery.  Here is another list from

 

11 Innovative Companies Using AI for Drug Discovery

Contributing Writer

By Sherri Gordon

Published December 15, 2025

Overview

A look at 11 companies using AI-driven platforms to reshape drug discovery, including generative models, computational chemistry, and data-centric biology.

 

For more articles on AI and healthcare on this Open Access Scientific Journals please see our Portals at

Medicine with GPT-4 & Chat GPT

AGI, generativeAI, Grok, DeepSeek & Expert Models in Healthcare

Artificial Intelligence: Genomics & Cancer

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Targeting amyloidopathy

Posted in Neurological Diseases, Neuroscience, Pharmaceutical Analytics, Pharmaceutical Discovery, tagged , , , , , on June 21, 2016| Leave a Comment »

Targeting amyloidopathy

Larry H. Bernstein, MD, FCAP

LPBI

 

Targeting a rare amyloidotic disease through rationally designed polymer conjugates

Inmaculada Conejos–Sánchez, Isabel Cardoso, Maria J. Saraiva, María J.Vicent
Journal of Controlled Release 178 (2014), 95–100
Saraiva et al. discovered in 2006 a RAGE-based peptide sequence capable of preventing transthyretin (TTR) aggregate-induced cytotoxicity, hallmark of initial stages of an inherited rare amyloidosis known as Familial Amyloidotic Polyneuropathy (FAP). To allow clinical progression of this peptidic sequence as FAP treatment, a family of polymer conjugates has been designed, synthesised and fully characterised. This approach fulfills the strategies defined in the Polymer Therapeutics area as an exhaustive physico-chemical characterisation fitting activity output towards a novel molecular target that is described here. RAGE peptide acts extracellularly, therefore, nointracellular drug delivery was necessary. PEG was selected as carrier and polymer–drug linker optimisation was then carried out by means of biodegradable (disulphide) and non-biodegradable (amide) covalent bonds. Conjugate size in solution, stability under invitro and in vivo scenarios and TTR binding affinity through surface plasmon resonance (SPR) was also performed with all synthesised conjugates. In their in vitro evaluation by monitoring the activation of caspase-3 in Schwann cells, peptide derivatives demonstrated retention of peptide activity reducing TTR aggregates (TTRagg) cytotoxicity upon conjugation and a greater plasma stability than the parent free peptide. The results also confirmed that a more stable polymer–peptide linker (amide) is required to secure therapeutic efficiency.

Polymer therapeutics are well established as successful first generation nanomedicines for treatment of infectious diseases and cancer[1]. Polymer–protein, drug and aptamer conjugates are innovative chemical entities capable of improving bioactive compound properties and thus increasing efficacy and decreasing toxicity[2,3]. Design of second generation of conjugates is now focussing on improved polymer structures, polymer–based combination therapy and novel molecular targets with great potential to further progress the clinical importance of these unique technologies [4]. Novel conjugates for the treatment of neuropathological disorders are proposed in this study. Amyloidosis is well known in the form of Alzheimer’s and Parkinson’s disease, but the target disease here is a rarer pathological disorder named familial amyloid polyneuropathy (FAP). FAPs constitute an important group of inherited amyloidosis diseases, and one of the most commonFAPs is caused by a mutated protein called transthyretin (TTR), which forms amyloid deposits, mainly in the peripheral nervous system [5]. The aggregation cascade of this mutated protein, produces a TTR aggregate (TTRagg) able to trigger neurodegeneration through engagement with the receptor-for-advanced-glycation-end-products (RAGE) which is present on peripheral neurons. RAGE signalling has been defined to be involved in many human pathologies such as Alzhehimer’s disease, diabetes and ageing, among others. This receptor is also up-regulated in tissues fromFAP patients [6]. The secreted RAGE form, named soluble RAGE (sRAGE), acts as a decoy to trap ligands and prevent interaction with cell surface receptors. sRAGE was shown to have important inhibitory effects in several cell cultures and transgenic mouse models, in which it prevented or reversed full-length RAGE signalling.

Saraiva et al. [7] discovered a specific peptidic sequence (named RAGE peptide) that is able to suppress TTRagg-induced cytotoxicity in cell culture. A reduced version of that peptide was proved to maintain the activity and the affinity of the initial peptide. The final peptide (compound A) contains 6 amino acids and responds to the sequence (from N to C terminus): YVRVRY. Although this provides an opportunity to design novel therapeutics for FAP treatment, peptide therapeutics themselves display well known challenges for in vivo use, e.g. low stability, poor pharmacokinetics and potential immunogenicity. Moreover the RAGE peptide demonstrates low solubility in plasma limiting its potential for i.v.administration.

……

Herein, novel specific nanoconjugates for the treatment of amyloidosis, and in particular familial amyloidotic polyneuropathy are reported. Apart from the research reported by Prof Arima et al. [22] using a hepatocyte-targeted FAP siRNA complex with lactosylated dendrimer (G3)/α-cyclodextrin(Lac-α-CDE(G3)), no other type of polymer therapeutic has been reported up to now for the treatment of this chronic degenerative family of diseases. Our rational design started from an active biomolecule of peptidic nature (RAGE peptide) that recognises the TTR prefibrillar aggregates responsible to promote cell death in FAPpatients [7]. The clinical progress of this promising inhibitor was masked by the well-known limitations of peptides, such as low solubility, low stability and possible immunogenicity. PEGylation through various linking strategies was successfully accomplished here as a solution for the named drawbacks, using a systematic approach to maintain peptide activity and receptor binding specificity. The data relating toTTR binding affinity, conjugate linker stability and the conjugate size distribution in solution of PEG– RAGE peptide conjugates indicate that the conjugates containing amide linkers have the greatest potential for further development as FAP inhibitors. Moreover, this novel conjugate has promising possibilities as a FAP therapeutic to be used alone in the early stages of the disease or as part of rationally designed combination therapy [23,24]. Preliminary in vivo studies (biodistribution) are shown in the supporting information demonstrating the enhanced plasma stability of the peptide upon conjugation (Fig.5S) , showing nospecific accumulation in any organ and renal excretion. More exhaustive in vivo experiments are currently ongoing with selected conjugates.

 

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Drug ‘Chemputer’

Posted in 3D Printing for Medical Application, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Investment, Population Health Management, tagged , , , on September 10, 2015| Leave a Comment »

Drug ‘Chemputer’

Curator: Larry H Bernstein, MD, FCAP

 

Revised 9/30/2015

 

The ‘chemputer’ that could print out any drug

When Lee Cronin learned about the concept of 3D printers, he had a brilliant idea: why not turn such a device into a universal chemistry set that could make its own drugs?

http://www.theguardian.com/science/2012/jul/21/chemputer-that-prints-out-drugs

 

Professor Lee Cronin is a likably impatient presence, a one-man catalyst. “I just want to get stuff done fast,” he says. And: “I am a control freak in rehab.” Cronin, 39, is the leader of a world-class team of 45 researchers at Glasgow University, primarily making complex molecules. But that is not the extent of his ambition. A couple of years ago, at a TED conference, he described one goal as the creation of “inorganic life”, and went on to detail his efforts to generate “evolutionary algorithms” in inert matter. He still hopes to “create life” in the next year or two.

At the same time, one branch of that thinking has itself evolved into a new project: the notion of creating downloadable chemistry, with the ultimate aim of allowing people to “print” their own pharmaceuticals at home. Cronin’s latest TEDtalk asked the question: “Could we make a really cool universal chemistry set? Can we ‘app’ chemistry?” “Basically,” he tells me, in his office at the university, with half a grin, “what Apple did for music, I’d like to do for the discovery and distribution of prescription drugs.”

The idea is very much at the conception stage, but as he walks me around his labs Cronin begins to outline how that “paradigm-changing” project might progress. He has been in Scotland for 10 years and in that time he has worked hard, as any chemist worth his salt should, to get the right mix of people to produce the results he wants. Cronin’s interest has always been in complex chemicals and the origins of life. “We are pretty good at making molecules. We do a lot of self-assembly at a molecular level,” he says. “We are able to make really large molecules and I was able to get a lot of money in grants and so on for doing that.” But after a while, Cronin suggests, making complex molecules for their own sake can seem a bit limiting. He wanted to find some more life-changing applications for his team’s expertise.

A couple of years ago, Cronin was invited to an architectural seminar to discuss his work on inorganic structures. He had been looking at the way crystals grew “inorganic gardens” of tube-like structures between themselves. Among the other speakers at that conference was a man explaining the possibilities of 3D printing for conventional architectural forms. Cronin wondered if you could apply this 3D principle to structures at a molecular level. “I didn’t want to print an aeroplane, or a jaw bone,” he says. “I wanted to do chemistry.”

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Cronin prides himself on his lateral thinking; his gift for chemistry came fairly late – he stumbled through comprehensive school in Ipswich and initially university – before realising a vocation for molecular chemistry that has seen him make a series of prize-winning, and fund-generating, advances in the field. He often puts his faith in counterintuition. “Confusions of ideas produce discovery,” he says. “People, researchers, always come to me and say they are pretty good at thinking outside the box and I usually think ‘yes, but it is a pretty small box’.” In analyzing how to apply 3D printing to chemistry, Cronin wondered in the first instance if the essentially passive idea of a highly sophisticated form of copying from a software blueprint could be made more dynamic. In his lab, they put together a rudimentary prototype of a chemical 3D printer, which could be programmed to make basic chemical reactions to produce different molecules.

 

First Complete Structural Study Of A Pegylated Protein

http://www.technologynetworks.com/Proteomics/news.aspx?ID=183266

Significant data obtained at NUI Galway reports first crystal structure of a protein modified with a single PEG chain.

Protein PEGylation is a technique routinely used to improve the pharmacological properties of injectable therapeutic proteins. PEG stands for polyethylene glycol, a synthetic polymer that is attached to proteins. The PEG chain artificially increases the size of the protein and improves its retention in the bloodstream. By remaining longer in the blood stream the protein therapeutic is more effective than normal.

Since PEGylation was developed in the 1970s, PEGylated proteins have significantly improved the treatment of several chronic diseases, including hepatitis C, leukemia, arthritis, and Crohn’s disease. PEGylated interferon is one of the most powerful therapeutics used to treat chronic hepatitis. Despite their importance the structure of PEGylated proteins has remained elusive. Now the first crystal structure of a protein modified with a single PEG chain has been determined through research at NUI Galway.

This important research was developed at NUI Galway by Italian PhD student Giada Cattani working with Dr. Peter Crowley, the lead author of the paper. The work also involved collaboration with Dr. Lutz Vogeley from the School of Biochemistry and Immunology at Trinity College Dublin and the crucial X-ray data was collected at the Diamond synchrotron in Oxford, UK.

Commenting on the research findings Dr. Peter Crowley from the School of Chemistry, NUI Galway commented, “The crystal structure reveals an extraordinary double helical arrangement of the protein! It is significant that this data was obtained at NUI Galway, the only Irish University to offer a degree programme in Biopharmaceutical Chemistry. This attractive programme provides training in an area that is essential for the development of new medicines and contributes to the Irish economy.”

A common approach to understand proteins is to crystallize them and determine their structure by using X-ray crystallography. This is necessary to understand what the protein looks like and how it functions. Thousands of research papers have been published about PEGylated proteins. Until the recent findings at NUI Galway there had been no success in  crystallizing a PEGylated protein. The knowledge obtained by the Crowley lab has implications for understanding how PEGylated proteins work. The NUI Galway team is also looking at ways to engineer protein assemblies based on this result.

 


Drugs Go Under Cover as Platelets to Destroy Cancer

  • Scientists say they have for the first time developed a technique that coats anticancer drugs in membranes made from a patient’s own platelets, allowing the drugs to last longer in the body and attack both primary cancer tumors and the circulating tumor cells that can cause a cancer to metastasize. The work reportedly was tested successfully in an animal model.
  • “There are two key advantages to using platelet membranes to coat anticancer drugs,” says Zhen Gu, Ph.D., corresponding author of a paper on the work and an assistant professor in the joint biomedical engineering program at North Carolina State University and the University of North Carolina at Chapel Hill. “First, the surface of cancer cells has an affinity for platelets; they stick to each other. Second, because the platelets come from the patient’s own body, the drug carriers aren’t identified as foreign objects, so last longer in the bloodstream.”
  • “This combination of features means that the drugs can not only attack the main tumor site, but are more likely to find and attach themselves to tumor cells circulating in the bloodstream, essentially attacking new tumors before they start,” adds Quanyin Hu, a Ph.D. student and lead author of the paper (“Anticancer Platelet-Mimicking Nanovehicles”), which appears in Advanced Materials
  • Here’s how the process works. Blood is taken from a patient (a lab mouse in the case of this research) and the platelets are collected from that blood. The isolated platelets are treated to extract the platelet membranes, which are then placed in a solution with a nanoscale gel containing the anticancer drug doxorubicin (Dox), which attacks the nucleus of a cancer cell.
  • The solution is compressed, forcing the gel through the membranes and creating nanoscale spheres made up of platelet membranes with Dox-gel cores. These spheres are then treated so that their surfaces are coated with the anticancer drug TRAIL, which is most effective at attacking the cell membranes of cancer cells.
  • When released into a patient’s bloodstream, these pseudo-platelets can circulate for up to 30 hours as compared to approximately six hours for the nanoscale vehicles without the coating. When one of the pseudo-platelets comes into contact with a tumor, three things happen more or less at the same time.
  • First, the P-Selectin proteins on the platelet membrane bind to the CD44 proteins on the surface of the cancer cell, locking it into place. Second, the TRAIL on the pseudo-platelet’s surface attacks the cancer cell membrane. Third, the nanoscale pseudo-platelet is effectively swallowed by the larger cancer cell. The acidic environment inside the cancer cell then begins to break apart the pseudo-platelet, thus freeing the Dox to attack the cancer cell’s nucleus.
  • In a study using mice, the researchers found that using Dox and TRAIL in the pseudo-platelet drug delivery system was significantly more effective against large tumors and circulating tumor cells than using Dox and TRAIL in a nano-gel delivery system without the platelet membrane.
  • “We’d like to do additional pre-clinical testing on this technique,” notes Dr. Gu. “And we think it could be used to deliver other drugs, such as those targeting cardiovascular disease, in which the platelet membrane could help us target relevant sites in the body.”

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