Feliza Mirasol is the science editor for 

Feliza Mirasol

Amgen and TScan Therapeutics, a US-based clinical-stage biopharmaceutical company specializing in T cell receptor-engineered T-cell therapies, announced on May 9, 2023 that they have entered into a multi-year collaboration under which they will use TScan's proprietary target discovery platform, TargetScan, to identify antigens recognized by T cells in patients with Crohn's disease. Under the agreement, TScan will receive a $30 million upfront payment. The company is also eligible to earn more than $500 million upon achievement of preclinical, clinical, regulatory, and commercial milestones as well as tiered single-digit royalty payments.

For its part, Amgen will evaluate a variety of modalities as it seeks to create therapeutics based on targets discovered by TScan. Amgen will retain all global development and commercial rights. It also has an option, under certain terms, to expand the collaboration to ulcerative colitis. Each party will handle its own research expenses.

"Anti-inflammatory drugs have traditionally been the standard of care for patients suffering from inflammatory bowel disease, but often lack efficacy and durability," said Raymond Deshaies, senior vice-president of Global Research at Amgen, in a company press release. "TScan's platform provides [an] approach to identify non-conventional drug targets to enable the development of potential first-in-class therapeutics to address unmet medical needs."

"We're excited to apply our target discovery platform to the autoimmunity space," said Gavin MacBeath, acting CEO and chief scientific and operating officer at TScan, in the release. "Our TargetScan platform, which we have now extended to identify MHC [major histocompatibility complex] class II targets of CD4+ T cells, is well-suited for the discovery of antigens targeted by the immune system in inflammatory bowel disease. We look forward to developing the value of our platform both in this partnership with Amgen and in other autoimmune diseases."

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In a deal potentially worth more than $530 million, Amgen will use TScan Therapeutics’ target discovery platform to home in on novel targets for Crohn’s disease treatment.

Amgen and Tscan Therapeutics Team Up to Identify Novel Targets in Crohn's Disease

On May 09, 2023, Sandoz, a Novartis division, and Just – Evotec Biologics, a US-based wholly owned subsidiary of Evotec SE that specializes in using artificial intelligence (AI)/machine learning (ML) technologies for biotherapeutic development, announced a multi-year partnership. Under the agreement, the companies will develop and manufacture multiple biosimilar medicines.

Sandoz will have access to Just – Evotec Biologics’ AI/ML-driven drug substance development platform and manufacturing technology, which Sandoz will use to expand its current biosimilars pipeline from 15+ to 24 assets. Sandoz also intends to continue development of its early-stage biosimilars pipeline through the partnership.

“This strategic partnership is founded on a strong shared sense of purpose and commitment to use disruptive technology with lower operational costs to deliver high-quality biosimilars at scale to patients around the world,” said Sandoz CEO Richard Saynor in a company press release.

“It provides us with additional capabilities to support the strategic expansion of our pipeline with new assets and to begin immediately transitioning our early biosimilar pipeline—ensuring continuity in development and manufacturing while Sandoz finalizes its planned separation from Novartis. Development of the biosimilars will ramp-up over the coming 12–18 months,” Saynor also said in the release.

The new partnership between Sandoz, a Novartis division, and Just – Evotec Biologics will aim to develop and manufacture multiple biosimilars.

Sandoz and Just – Evotec Biologics Form Biosimilars Pact

The bio/pharma industry is building on the small successes of continuous manufacturing (CM) for the advanced manufacture of bio/pharmaceuticals. With FDA throwing its support behind CM as part of a bigger initiative to progress advanced technologies for biopharma manufacturing, it remains to be seen whether CM technologies will see an uptick in adoption within the industry. In this episode of 

 editors Chris Spivey and Feliza Mirasol discuss with industry experts the advantages and savings that CM may offer as well as the very real obstacles that might make a fully continuous process not feasible, especially in biologics manufacturing.

, owner of Pharmaceutical Engineering Solutions, is a consultant to the pharmaceutical industry working at the interface of technology, R&D, manufacturing, and corporate strategy. He spent nearly 40 years in big pharma, working for Merck, Bristol Myers Squibb (BMS), and Janssen, a Johnson & Johnson company. At Merck and BMS he worked in R&D, rising to vice-president of Process Development at BMS. During those 28 years he was lucky to work on numerous important compounds that went to market, including Primaxin, Mevacor, Proscar, Crixivan , Reyetaz, Entecavir, Muraglitazar, Sprycel, Onglyza (through FDA’s Quality by Design pilot program), Apixiban, Dapagliflozin, and Brivanib.At Janssen, he spent more than 10 years working on manufacturing technology, focusing on integrating data analytics, sensors and models to realize real-time quality awareness and real-time release when possible, along with continuous manufacturing and other manufacturing modalities, covering small molecules, monoclonal antibodies, vaccines, and cell and gene therapy.

 is faculty of the Department of Chemical and Biochemical Engineering, Rutgers University, NJ, and director of pharmaceutical systems engineering. He is the recipient of the prestigious European Federation of Chemical Engineering (EFCE) Excellence Award. His research focus is continuous manufacturing of drug substance and product. He is principal investigator (PI)/co-PI of several projects funded by FDA, National Science Foundation, and companies. He has published more than 75 papers, edited one pharmaceutical systems engineering book published by Elsevier, written more than 12 book chapters, and presented at more than 130 conferences. He is actively serving as a journal editorial board member, and conference session chair.

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In this episode of Drug Digest, Pharmaceutical Technology editors discuss the advantages and savings that continuous manufacturing may offer as well as the very real obstacles that might make a fully continuous process not feasible, especially in biologics manufacturing.

Drug Digest: Advancements in the Implementation of Continuous Manufacturing

With the explosion of RNA-based therapeutics on the scene, development projects are ramping up to bring these and other nucleic acid-based therapeutics to market. But what exactly are nucleic acid-based drugs and what advantages do they hold over conventional therapeutics (biologics and small-molecule)?

According to Carl Schoellhammer, PhD, principal at DeciBio Consulting, a US-based life science strategy consulting firm, nucleic acid-based therapeutics are drugs that are made up of a string of nucleotides; these include DNA and RNA-based medicines. These types of therapeutics can act at the genetic level to potentially correct or restore healthy function at the level of a patient’s genes.

Vasant Jadhav, PhD, senior vice-president of Research and head of the RNAi Platform group at Alnylam Pharmaceuticals adds that a common feature of nucleic acid-based therapies is that they are “programmable” drugs; a nucleic acid sequence provides the “code” for the intended target. Examples of approved nucleic acid-based therapies include RNA interference (RNAi), antisense oligonucleotides (ASOs), and the now-familiar messenger RNA (mRNA) therapeutics.

RNAi therapeutics utilize a post-transcriptional gene silencing mechanism in which the active ingredient is double stranded short interfering RNA (siRNA). The siRNA targets and degrades mRNA encoded by a disease-causing or disease-associated gene, explains Jadhav. “RNAi therapeutics leverage a naturally occurring protein complex, called RISC [RNA-induced silencing complex], to accomplish gene silencing,” he states.

Meanwhile, ASOs, also a gene silencing medicine, are composed of single-stranded oligonucleotides that hybridize to target RNA and degrade it by “either recruiting RNAseH [ribonuclease H] (so called gapmer design) or modulate splicing to produce desired mRNA (so called splice switching olignucleotides),” according to Jadhav.

Finally, as has been seen with the COVID-19 vaccines, mRNA therapeutics are experimental genetic medicines in which the mRNA itself is the therapeutic entity; it is introduced with the aim of expressing a functional/therapeutic protein or antigen. However, there are currently no approved mRNA therapeutics outside of the COVID-19 vaccines, Jadhav observes.

The COVID-19 vaccines are currently the most well-known type of medicine that utilizes a nucleic acid-based active ingredient (i.e., mRNA). However, as described previously, nucleic acid molecules can be used for other types of medicines (e.g., as gene-silencing medicines).

Schoellhammer emphasizes the fact that nucleic acid-based therapeutics “are a powerful set of medicines with the capacity to turn signals on, or off, at a genetic level.” He points out that there are well over 10 nucleic acid-based therapeutics that are approved by regulators. “These medicines are, broadly speaking, divided by the action they take in the body to either turn signals on or off,” he states.

ASOs, as well as siRNAs, for example, target genes to turn them off or limit their expression. And while most of these approved nucleic acid-based therapeutics are for rare diseases (i.e., small patient population), some have been indicated for vast patient populations: Schoellhammer points to a Novartis product, Leqvio (inclisiran), which received approval by FDA for treating a type of high cholesterol in late 2021 (1).

Inclisiran, Schoellhammer explains, is a siRNA medicine that shuts off a signal in the liver, leading to a lowering of lipids. While it is currently approved for a subsegment of individuals with high cholesterol, he expects that the therapeutic could in the future be applied to anyone with high cholesterol, therefore making these medicines mainstream.

“On the other end of these therapies sits mRNA, which tells the body to produce, or turn on, specific proteins that the mRNA encodes for. [Messenger] RNA is like a blueprint, telling our cells how to make certain proteins. If a certain protein is desired, the corresponding mRNA can be made and delivered so the body will produce it,” Schoellhammer adds.

This is how the COVID mRNA vaccine operates; it is a sequence that encodes for a small fragment of the spike protein in the SARS-CoV-2 virus, for example. “When our body’s cells produce this small protein fragment, they recognize it as foreign, and produce an immune response, just as a traditional vaccine does,” Schoellhammer says.

“The pandemic demonstrated the power of this therapeutic and interest has flourished, with 100s of companies working to create new mRNA-based medicines and well over 200 public programs in development,” Schoellhammer continues. “These programs are tackling a diverse set of applications, ranging from oncology through the development of immune-stimulating treatments to help fight the cancer, to other indications leveraging CRISPR [clustered regularly interspaced short palindromic repeats]-based editing whereby the mRNA encodes for the CRISPR machinery.”

Nucleic acid-based therapeutics hold a definite advantage over conventional biologics or traditional small-molecule APIs. According to Schoellhammer, one of the greatest benefits of nucleic acid-based medicines is their specificity.

“In the case of those that turn off signals, they must find and bind with the exact complementary sequence in our body to turn it off. This means that other genetic signals can be unaffected. Similarly, for mRNA, the sequence, and therefore the protein it encodes for, is controlled. These features greatly limit any off-target effects that are often seen with small molecules or biologics,” Schoellhammer states.

Compared to small molecules and monoclonal antibodies, nucleic acid medicines, such as RNAi therapeutics work “upstream,” asserts Jadhav, treating diseases at their genetic source—that is, at the RNA/transcript level—rather than by addressing the symptoms/manifestations of the disease. Furthermore, Jadhav adds, “small-molecule drugs and monoclonal antibodies have inherent limitations when it comes to the speed with which they can be developed and the type and breadth of diseases they can be used to treat.”

Jadhav points out that the development of small molecules is an arduous process that relies on understanding the structure of the target disease-causing or disease-associated protein (vs. the genetic sequence that encodes that protein). “Oftentimes the structure of the protein is not known and needs to be solved. Identification of the right small molecule requires screening large libraries of chemical compounds to identify a candidate that can interact with the target protein in a ‘lock and key’ fashion. This is a painstaking process that requires extensive trial and error to achieve efficacy, tolerability, and safety,” he emphasizes.

As far as biologics such as antibodies are concerned, Jadhav explains that, while antibodies as a class of medicines have enjoyed much therapeutic success in the past decade, one of the drawbacks here is the inability to access and act on intracellular protein targets. “In contrast to monoclonal antibodies, nucleic acid medicines, such as RNAi therapeutics, can act on disease-causing or disease-associated proteins that are expressed both on the inside and outside of the cell,” he explains.

Meanwhile, Schoellhammer points out a limitation in nucleic acid-based therapeutic development: nucleic acid-based medicines are difficult to deliver, which is a drawback. To work, he specifies, they must be taken up by the right cell in the right place in the body. At the same time, nucleicacid-based medicines are extremely delicate and susceptible to being destroyed in the bloodstream.

“While this can somewhat limit the indications that scientists are using these medicines to treat, it further helps improve these medicines’ specificity and lack of off-target effects. For example, if one uses an siRNA therapeutic to ‘silence’ a target, dosing someone with more of that siRNA should have little effect. This is not the case for small molecules, such as aspirin, where taking more of it could eventually lead to significant, negative side effects,” Schoellhammer says.

Jadhav, meanwhile, explains that nucleic acid-based therapeutics, such as RNAi-based medicines, leverage a platform technology that gives rise to consistent and predictable drug properties. “This is not the case for small molecules, where each individual small molecule compound is completely unique, both in terms of its design elements and its profile as a drug candidate,” he states.

Development of nucleic acid-based therapeutics supports the bio/pharma industry’s shift to precision medicine. For instance, there are examples of nucleic acid-based medicines (e.g., RNAi therapeutics) that are approved to treat rare and ultra-rare genetic diseases for which there were no previously available therapeutic options, points out Jadhav. He has observed the therapeutic landscape of rare diseases dramatically evolve in large part due to the development and availability of genetic medicines, which now have the potential to help patients with more common diseases for which treatment options are limited or inadequate.

Schoellhammer says that, because these medicines are based on genetic sequences, they are highly specific. They can be used to treat rare diseases that are caused by a mutation in our genes. He points out that Biogen’s Spinraza (nusinersen), an antisense therapy for treating spinal muscular atrophy, was a significant accomplishment that has drastically improved the quality of life in patients for whom the disease often presents in newborns and is fatal by age two.

“Beyond presently approved nucleic acid medicines, companies are working on using these therapeutics in combination with CRISPR machinery to enable highly precise edits to genetic signals in patients. In theory, this could one day be used to target a drug for a unique individual,” says Schoellhammer.

Adding to the complex tapestry of nucleic acid-based therapeutics are the different moieties, or species of molecule, within the nucleic acid space. According to Schoellhammer, there are several different types based on their action. Within ASO therapies, for example, there are a variety of strategies to control the “degree” of silencing. There are certain therapeutics, such as Spinraza, for example, that are termed “exon-skipping” because, rather than telling the body to turn off a gene completely, these therapeutics instruct the body to skip only a certain part of a gene when producing the corresponding signal, Schoellhammer explains.

He also emphasizes the existence of microRNA (miRNA), which can have a similar effect to that of silencing therapies, guide RNAs (gRNA), which are used to target a desired gene sequence in the body for editing using CRISPR machinery, and, finally, certain types of RNA medicines that utilize naturally occurring machinery in the body to elicit an editing effect on defective genes. The latter is referred to as transfer RNA (tRNA).

“Beyond this, there is an almost endless further segmentation based on chemical modifications made to the nucleic acid therapy to make it more resilient to degradation by our body or make it more efficient in eliciting its desired effect,” Schoellhammer concludes.

1. Novartis. FDA Approves Novartis Leqvio (inclisiran), First-in-Class siRNA to Lower Cholesterol and Keep it Low with Two Doses a Year. Press Release, Dec. 22, 2021.

The market potential of nucleic acid-based therapies have been pushed into the spotlight following the success of the COVID-19 vaccines.



Ramping Up Nucleic Acid Therapy Development

Digitalization, artificial intelligence (AI), and machine learning (ML) are several of the major tools being used in the bio/pharmaceutical industry today to implement significant leaps in optimization (e.g., reducing costs, shortening timelines, streamlining processes, improving product quality, etc.). Importantly, these tools are being used early in the drug development cycle, from R&D/drug discovery through to clinical-phase development, and then onward in commercial manufacture. A closer look at the implementation of these tools can shed light on the importance of smart drug development in the early discovery phase. For instance, how does using “smart” tools have an impact on the development process going into the clinical phases?

The existing drug development paradigm is based on moving through several stages, sequentially, says Jo Varshney, PhD, founder and CEO of VeriSIM Life, a US-based company specializing in the use of advanced AI and ML techniques for drug discovery and development. Drug development moves from the identification of a disease or condition target to validating a safe and effective drug candidate in human clinical trials, she notes.

“This paradigm suffers from tremendous waste and cost. Passing from one stage-gate to the next is a siloed process where data [are] often not shared and poor proxies define success for human biology—especially animals,” Varshney remarks.

Smart drug development re-imagines the traditional drug development process by allowing for design and validation early in discovery through the use of better models of human biology and more comprehensive data. This allows for the process to be “parallelized,” where, typically, steps such as dosing or formulation can be factored in conjunction with hit-to-lead selection, Varshney notes. “Smart drug development quickly identifies dead-end outcomes so you can course correct before investing in heavy experimentation, which saves time and costs, getting better drugs to patients faster,” she emphasizes.

“‘Smart’ drug development has always been with us,” says Grant Wishart, PhD, senior director, Small Molecule Drug Discovery and Logica Lead, Charles River Laboratories. Wishart points out that the bio/pharma industry has generated knowledge from past experiences and developed and leveraged new technologies to bring new medicines and safer medicines to patients for increasingly complex diseases.

“From rational approaches in the 1960s pioneered by the Scottish pharmacologist, Sir James Black, to computational structure-based design emerging in the 1980s—but perhaps not fulfilling its potential until the early 2000s—there have been many ‘smart’ enhancements to drug discovery and development,” Wishart explains. “Each of these has influenced and advanced what we do to a greater or lesser extent.”

Wishart also points out that organizations continuously strive for “smarter” drug development to reduce discovery timelines and increase certainty in clinical progression. “Today we are at the intersection of advances in data generation, automation, and advanced computation, which come together to offer potential step changes in how we discover and develop drugs of the future,” he remarks.

Digitalization is a major trend in the bio/pharm industry today. Advances in digitalization and other tools have allowed the industry to organize and connect data sets as well as set up more efficient workflows, says Wishart. Smart tools such as digitalization have also allowed the industry to connect disparate data sets from data management systems and instruments across the continuum.

According to Wishart, this connectivity helps the industry to manage information along individual programs. Moreover, it also helps in the aggregation of data to progress ML/AI modeling toward better prediction and assistive tools, he says.

“By doing this, we also have the opportunity to reduce the overall timelines for researchers to match data sets shared from various partners across the ecosystem, including academia, CROs [contract research organizations], and CDMOs [contract development and manufacturing organizations],” Wishart states. “Today, scientists spend a tremendous amount of time doing this, and we envision a world where there is strong interoperability of data between organizations across all phases of drug discovery.”

Meanwhile, Varshney emphasizes the point that smart drug development is dependent on digitalization. She explains that in-silico-based approaches, whether labeled “Model Informed Drug Development” or “digital twin,” bridge the gap between human biological process and understanding those processes by leveraging tremendous amounts of data from existing scientific resources and custom-developed assays.

“Digitalization enables the conversion of analog data for the purposes of complex analysis and machine learning. This opens the door to a high-powered computational understanding of biological processes and predictions about how drugs will interact with them,” Varshney states. She further adds that digitalization also reduces the “black box” of animal testing, in which experiments often succeed only to fail inexplicably in humans.

Tools such as AI, automation, ML, and digitalization are already being utilized for bio/pharmaceutical manufacturing processes, but how are they being leveraged in the R&D and drug discovery/drug screening phases?

According to Varshney, one use for AI and ML is generating compound hits for a given target, a process that typically screens for molecular structures that can bind to the target from billions of potential molecules. Another use she points out is the prediction of various characteristics of one or more (e.g., 1000s) molecules, including the mechanism of action in the body, toxicity, and more—across different dosing, routes of administration, and formulations. “These predictions can then be used to rank compounds for potential success and failure well before clinical trials, reducing the financial risk associated with developing a drug candidate,” she adds.

AI and ML techniques can also be used to understand how drug candidate efficacy may vary in different patient populations, Varshney continues. These techniques can be used to inform trial design, dosing, and combination therapies, among other things. “Generating predictive patient stratification analysis in this way can improve clinical success and ultimately move us closer to precision medicine care,” she states.

Wishart notes that the industry is seeing a huge interest in applying AI/ML and automation to all steps of the drug discovery process and across all modalities. In target discovery, for instance, huge volumes of data are being linked to select disease-modifying targets. Meanwhile, preclinical and clinical data are being used to select the patient populations for which the drug is beneficial. However, Wishart adds, the area of greatest personal interest is in the early discovery phase (small molecules) where generative models are being used to design compounds optimized in their scoring versus predictive models for parameters of interest, such as on-target potency, early absorption, distribution, metabolism, and excretion (ADME), and selectivity. Using generative and predictive models in this way allows for the ability to better choose compounds for synthesis and testing.

“Success is highly dependent on the predictive utility of such models. As a minimum, models must be able to remove the compounds with the lowest probability of success of meeting the desired profile. Although expectations are typically much higher where we are striving for quantitative predictions of in-vitro data and extending our modeling efforts to pharmacokinetics, efficacy, and safety,” Wishart says.

For example, Charles River, with its partner, Valo Health, created an end-to-end AI-driven approach to drug discovery—in this case small-molecule discovery—called Logica, Wishart says. This approach combines computational AI and experimental approaches to hit identification and optimization. “Ligand-based computational models are used to trawl vast areas of chemical space for potential hit series. If there is no literature data to develop the computational models for the target, then Logica simply leverages experimental high throughput screening (HTS) and DNA Encoded Library (DEL) screening to generate that initial data and to fuel hit finding and model building,” he explains.

In this scenario, hit compounds are optimized to advanceable leads and, ultimately, to clinical candidates through AI/ML-guided approaches to de-risk candidate compounds and ensure that only the best candidates progress. “The Logica concept results in fewer design cycles, fewer compounds synthesized and tested, and enhanced decision-making on progression,” says Wishart.

“The opportunity to integrate the ‘design’ and ‘analyze’ components of the design-make-test-analyze (DMTA) cycle with advances in synthetic route prediction and automation in synthesis seems tantalizingly close,” Wishart continues. He notes that one can envision a near future state where drug discovery experts are driving parts of the drug discovery trajectory through almost closed loop systems comprising AI/ML-enhanced designs and automated synthesis, purification, and testing.

“The DMTA cycle for small-molecule discovery is, of course, a very specific area of drug discovery, and there are also many technology pioneers applying similar principles in the design, optimization, and development of other modalities, including antibodies and peptides,” Wishart states.

In enhancing the early R&D and drug discovery stages, the industry is seeking faster ways to generate, gain insights, and make decisions to get therapies to patients as fast as possible, emphasizes Wishart. One way to gain speed is through the appropriate management of preclinical trials. “The better we manage our preclinical data within organizations and across organizations, the higher probability we will have to make this [speed-to-patient] happen,” Wishart says. “By designing digital systems that simplify the collection, organization, visualization, and sharing of [these] data, we contribute to the progress toward smarter drug discovery. This starts with strong digital architecture, data standards, and connectivity of tool sets.”

It helps that, today, various organizations are working together to join the pieces together and leverage the advances in these tool sets, including AI, ML, and automation. “We see ourselves as a key player in the ecosystem of these technologies,” Wishart states.

“Hybrid AI and model-informed predictions of drug behavior inform the design of preclinical testing to be both prescriptive of testing and orthogonally confirming of testing,” adds Varshney. For example, she explains, AI-based predictions can guide which rat experiments to conduct and how to conduct them; in addition, they can also explain discrepancies and variabilities of observations, which can elucidate important disease pathways.

“Smart drug development is all about accelerating therapies to patients while reducing unnecessary research and development,” Varshney says. To that end, introducing techniques such as predictive analytics early in drug candidate discovery can eliminate dead-end compounds much more quickly. Narrowing the field with such techniques, before performing animal model tests that often result in false positives, can save millions of dollars in testing and assays, she asserts. Meanwhile, closing the early “translational gap” can increase the number of successful drug candidates entering clinical trials.

“Drug discovery is extremely difficult and is often a high-risk, high-reward industry,” Wishart states. “Costs for drug development are high, timelines are lengthy and are accompanied by high failure rates throughout the process. Therefore, elevating the probability of success through means of enhanced knowledge, technology, and automation is of the utmost importance,” he says.

While it is known that the most costly and lengthy phases of drug development are in the clinical phases, the actions taken in the early discovery phase can and will have a significant impact, Wishart stresses. Selecting the right target with the greatest certainty in modulating the disease in humans is imperative, and reducing timelines in the hit-finding and optimization phases can result in benefits to the patient (e.g., faster delivery of the therapeutic) and to the organization (e.g., additional patent life).

There is also potential for de-risking pre-clinical candidates during the optimization phases through the development of advanced safety and pharmacokinetics prediction models, Wishart adds. “Taken together, it is clear that ‘smarter’ drug discovery in the early stages can have a major impact on the downstream success, with the ultimate goal being to develop medicines for unmet medical needs and to get these medicines to patients as quickly and as safely as possible,” he says.

Among the misconceptions that persist around the “smart” technology evolution is the assertion that AI is unreliable. Varshney observes that many scientists believe AI and ML technology can never replace traditional scientific methods, and, therefore, these technologies cannot be trusted to provide value in drug discovery and development. “Much of this mistrust is due to the need for explainability found with many systems. But explainability is now being addressed by many algorithms and within individual technology platforms,” she explains.

For example, Varshney points to a hybrid AI technology utilized by VeriSIM Life (the company’s Translational Index technology), which scores the potential of a compound to succeed in the clinic. The technology also provides an itemized breakdown of the score’s components, which gives insights on what is working, what needs improvement, and recommendations on how things can be done differently for better outcomes. Insights generated may include weighted measures of parameters such as the drug’s properties, animal study outcomes, and first-in-human doses.

Meanwhile, Wishart notes that there are several points to consider around the use of these tools and technologies. For one, there is unlikely to be a magic “smart” solution to drug discovery that will elegantly transform drug discovery into a simple process. Drug discovery is much more complex than that and, as the industry has seen in the past, there will continue to be evolution and progress in the way new drugs are discovered and developed in the future. This evolution will largely be due to the emergence of new and exciting technologies.

“Perhaps the biggest challenges are twofold,” Wishart expounds. “First of all, managing our own expectations, both positive and negative, to truly understand where new developments will have the biggest impact. Secondly, understanding what needs to change to enable that impact—this can encompass co-technologies or deeper cultural changes necessary to embed such developments.” For instance, looking at the role of AI/ML in early drug discovery rather than simply using those tools to bolster current workflows can create competing workflows with traditional tried and tested approaches. This conflict can potentially lead to a challenging environment where the impact of the AI/ML approaches are thus not realized, Wishart cautions.

When referring to this article, please cite it as Mirasol, F. Using Smart Tools for Smart Development. 

The use of smart tools in early drug discovery can have an impact on downstream phases of drug development.

Using Smart Tools for Smart Development

Returning to the office on the back of a successful INTERPHEX 2023, and it seems the return of in-person conferences has been solidified as the world wakes up after an extended hibernation period of COVID quarantine. Spring is back, not just in the weather, but also in conference-goers’ steps!

INTERPHEX was back in its usual haunt at the Javits Center in New York City, where engineers, manufacturers, c-level executives, and others came together to forge new and nurture already established pharma and biotech relationships. For the 

 team, the Keynote stage was the platform where the editors shed light on specific key trends set to shape the industry moving forward.

Leading the charge, on April 25, 2023, was a session on mRNA Supplier-developer Relationships and Technical Advances, where discussion revolved around the continued progress of mRNA-based therapies, which, thanks to the mRNA vaccines, have been largely optimized for stability, structure, and delivery. The industry is looking to novel delivery methods for these innovative therapies as efforts to ramp up dose-loading capabilities have led to new intellectual property filings. The second session on Tuesday, Cell and Gene Therapy Scale Up and Process Optimization, saw experts exploring issues around the manufacturing of viral vectors, whose complexity requires components not needed in stable, recombinant protein-expressing production systems.

The morning of April 26, 2023, saw the exciting dialogue continue with the Organ on a Chip for Pharmaceutical Development session. Here, experts shared their insights and experiences in the up-and-coming technology of organ-on-a-chip, which is poised at a tipping point in the drug discovery arena as well as in the shift toward personalized medicine. The afternoon session on Wednesday, and close of the keynote sessions for INTERPHEX, focused on Applied Continuous and Modular Manufacturing Innovations, where experts evaluated how the bio/pharma industry is building on the small successes of continuous manufacturing (CM). With new industry guidance on CM (1), there may likely be increased use of CM technologies for a variety of therapeutics, including small molecules, biologics, vaccines, and other modalities.

If you missed out on INTERPHEX, session information and further conference coverage can be found on 

Guidance for Industry, Q13 Continuous Manufacturing of Drug Substances and Drug Products

When referring to this article, please cite it as Mirasol, F. Springing Forward. 

INTERPHEX was back in full swing with Keynote sessions highlighting cutting-edge technologies that are setting the stage for the bio/pharma industry’s future.