A conversation with Ben R. Taft, PhD., Senior Director of Chemistry, Via Nova Therapeutics

With an ever-growing body of knowledge and rapidly advancing technologies, the pace of change in the drug discovery industry is fast. Yesterday’s challenges turn into today’s opportunities, which then become commonplace in the world of tomorrow. In this ongoing article series, we share recent conversations we’ve had with drug discovery professionals that capture this intersection of challenge and opportunity. We hope you find these discussions as interesting and insightful as we have.

This first article presents a conversation with Ben R. Taft, PhD., Senior Director of Medicinal Chemistry at Via Nova Therapeutics, on challenges and opportunities in SAR studies. 

CAS: What has been the biggest change you’ve experienced in SAR studies since you entered the field?

Ben: Changes in the data world. The move toward digitalization in the industry has definitely been very impactful. In parallel, there’s also been an explosion of data science and tools for visualizing and interpreting data. With digitalization making data more accessible, we have seen the development of tools for connecting all that data—linking it together and associating it with other data—enabling teams to work much more efficiently. With these tools, we can identify trends in the data and make new observations that we weren't able to before. 

And then, on top of all that, there’s growth and development in machine learning (ML) and artificial intelligence (AI). When you put all of these pieces together, you’ll find that there are a lot of really exciting things happening in the data world.

CAS: I’m glad you mentioned ML—we’re hearing a lot about it these days. How are AI and ML affecting drug discovery and SAR?

Ben:  I think this field is really just getting started, but we’re already seeing impacts on structure predictions and optimization. I’m not an expert in this specific area but my understanding is that, at a very simple level, ML makes the biggest impact when you have large datasets. It’s useful for finding trends and insights in datasets that are too large and complex for humans to sort through, but which ML can analyze very quickly and efficiently. For example, if you have enough of the right kind of data, you can build models to help design new structures by predicting things like solubility, bioactivity on an enzyme, and so on.

A good example where the dataset is too large for human analysis is the use of DNA-encoded libraries for screening bioactivity. They're essentially generating billions of data points using DNA-encoded library screening and then their own custom ML algorithms to sort through that data and predict the best structures to synthesize and retest.

But, as my colleagues who work in this area are always reminding me, the output from any ML or AI project is only as good as the data that's going into the project. The quality of your model and its ability to make predictions that work in the real world is very much limited by the size of your dataset as well as the range and diversity of your data.

CAS: What do you think is the benefit of using AI and ML? Is it just to speed up your work, or do you think it’s enhancing what you do?

Ben: I think it’s helping us identify novel structures that we wouldn’t have otherwise predicted and make sure we’re not overlooking something as well as increasing efficiency. Think about it this way. In a typical project, you’ll synthesize 200 to 2,000 new analogs of a drug trying to fine-tune all the different physical, chemical, and biological properties before nominating a drug candidate out of that set of compounds. Each one of those compounds has ten to fifty pieces of data associated with it—that’s a lot of data.

While there are excellent tools for visualizing the data so we can look for trends, thresholds, and activity cliffs, there's still human error and the possibility of missing something. But with AI and ML, the models will suggest compounds to prioritize based on certain trends or observations and act as a backup to the scientist. They feed us additional data so we can make better decisions more efficiently.

However, ultimately, you still have to synthesize new compounds and get real data to make final decisions.

I think what people are hoping is that instead of having to make 200 to 2,000 new compounds to find a drug candidate, we would only need to make 20 to 30 compounds out of all the possible designs. Unfortunately, I don't think we're anywhere near that yet.

CAS: While AI and ML still have a long way to go, what role do you see them playing today for the drug discovery chemist?

Ben: I view them as an additional tool in the overall toolbox that drug discovery scientists have. At the end of the day, what we do is very complex and nuanced, and there’s so much uncertainty in translating a drug from in vitro studies to humans, that I don’t think AI will be taking jobs from chemists any time soon! We have to do all these safety and toxicology studies, working in different species of preclinical animals, before we ever think about putting a compound into a human because no matter how much data, software, and technology we have today, those studies are still the best predictor of what safety outcomes are going to be in a human.

The AI and ML tools we have today are supporting the work of drug discovery scientists and giving us additional insights.

CAS: Let’s pivot now from the virtual world to the wet lab. Where do you think the biggest bottlenecks are in small molecule drug discovery?

Ben: There are bottlenecks everywhere you look! One big one is the synthesis of new compounds. During lead optimization, you need to synthesize hundreds or even thousands of new analogs for each structure. With each analog taking as long as several weeks to synthesize, you’re talking about a big expenditure of time and money, especially when you factor in all the time scientists are spending coordinating all these efforts.

Then, once you get the analogs in, you need to run them through a set of dozens of assays, collect the data, and start the analysis part of the cycle that we just talked about.

CAS: Do you see any good solutions to this bottleneck?

Ben: One technology I’m looking forward to is microscale chemistry platforms. Microscale chemistry platforms enable the rapid synthesis and purification of tens to hundreds of new drug molecules in plate format in parallel, utilizing state-of-the-art robotics and software. These platforms are exciting because, in theory, they enable the cycle of design, synthesize, test, analyze, and back to design much more quickly than conventional methods, and they generate more data faster. The hope is that you'll be able to identify the best drug analogs faster and get to decisions faster.

What I like about these platforms is that they generate real data, not the calculated or predicted data that ML and AI platforms generate. They’re not helping you prioritize analogs to study but getting the experiment done so you can make solid decisions now.

Ben: This discussion of predicted data and empirical data raises an important point I’d like to make about technology. What's become pretty clear to me over my time in the industry, and just by being a scientist in general, is that we spend a lot of time talking about different technologies and different strategies. Individually, these different technologies are often really great tools, but I have never seen a situation where one technology or strategy applies to every single project.

To be a really great drug discovery scientist, you have to be well versed in all the different technologies and tools and strategies and evaluate their suitability for each project. There will always be caveats or differences between individual drug research projects that make one situation different from the other.

For example, AI is not going to help every project. There are so many different things you have to evaluate—the target, the drug product profile, the disease, the patient population, how the drug is administered, where the drug is administered—and all these different factors affect each project, making that project unique and different. No single tool like AI is always going to be the right solution for every project.

CAS: That’s a great point about choosing the right technology for your project! Let’s switch gears and talk about drug discovery in general, starting with small molecules. Why develop small molecule therapeutics? We’ve now got protein and antibody therapeutics, cell and gene therapies, antibody-drug conjugates, antisense oligonucleotides—where do small molecules fit in?

Ben: That’s a great question and it makes the point I was just talking about—you need the right technology for the job and there is no one-size-fits-all solution to any problem! There are certain things that antibodies are great at, right? They have a super long half-life in plasma, so you can dose once a month, and they have extraordinarily efficient target binding capacity. But on the limitation side, they're really expensive to manufacture, difficult to make stable, challenging to distribute, and have to be injected, which is not the ideal route of administration. Lastly, and maybe most importantly, scientifically, they don't cross cell membranes much at all unless they're uniquely designed or engineered. So you can't target any intracellular or intramembrane biological targets unless they extend out beyond the cell membrane or the tissue.

This is probably the biggest distinguishing factor between small molecules and biologics in general—with small molecules, you can optimize the properties to get into any tissue type you want, in any part of the cell compartment you want. In addition, in parallel, you can optimize the ADME or DMPK properties so that the drug can be administered in an oral tablet or capsule, which has proven to be the most preferred way that patients like to take drugs.

Small molecules are also typically cheaper to manufacture and have better storage, stability, and distribution properties.

But, again, there are going to be scenarios where your drug research program is perfectly suited for a biologic or any of these other new therapeutic modalities like cell therapies, radioligands, CRISPR, and so on.

There are all kinds of exciting new things coming into the market now and being developed, but no one of those technologies is going to apply to every single drug discovery project.

CAS: Speaking of drug projects, can you tell us a little bit about what your mission is at your current company, Via Nova Therapeutics?

Ben: Sure! We’re trying to make an impact on the important viral diseases that big pharma is ignoring. Via Nova was spun out from Novartis by Don Ganem and Kelly Wong. We wanted not only to continue working on the programs we'd already started, but also to dive into new research areas, focusing on viral diseases that big pharma is not adequately resourcing.

Big pharma typically doesn’t put much effort into viral diseases unless they are chronic, like hepatitis and HIV. But there’s a lot of unmet need beyond this. COVID was a big reminder of that. At Via Nova, we’re working on acute and subacute viral diseases, many of which don’t have any treatments, such as BK polyomavirus.

CAS: For our last question, we’re going to give you a magic wand to fix anything you want in the drug discovery process. What would you fix?

Ben: I think the biggest problem in our industry is really two-fold. First, the general public doesn't really understand how drugs are discovered and how much time, effort, and money it takes to develop new medications.  Better transparency and education about the biopharma industry would benefit everyone.

Second, I think our paradigm for how drug discovery and development research is funded is somewhat limited because it is essentially all privately funded. The money comes from the investment world or the financial world, and the drivers are all capitalistic. The projects that get the most support are not necessarily the ones that are most important to patients, but the ones that are determined to have the most money-making potential. Those decisions are all trickled down to the scientific level where a scientist might have a brilliant idea for a new drug that would completely cure a disease where there's no medication currently available. But if there are limited numbers of those patients worldwide, then it’s not a viable business strategy and that project isn’t likely to get support.

I think this whole issue of how medical research and drug discovery research is prioritized and funded will, in the long term, have some negative consequences on which diseases are prioritized and how much medicines cost. More general awareness and education about how our industry is funded and how challenging it is to get funding could lead to a wider pool of people thinking about how to solve this problem and generating new ideas and new models for how to fund research, either by the government or social sources. 

At the end of the day, I got into drug discovery to make medicines that can treat or even cure diseases. We should make sure that we make medicines that patients need, not just the ones that will generate the most profit.

This new peer-reviewed article from the ACS Chemical Neuroscience Journal reveals how the microbiome affects our brain, mental health, and can be related to autism, schizophrenia, and even Alzheimer's. This explores the impact that gut microbes can have on various parts of brain function and options to improve beneficial bacteria.

It uncovers emerging research around improving the gut microbiome with probiotics, prebiotics, fecal transplants, dietary interventions, and more. It also highlights some of the challenges and limitations of this field of research while proposing some future directions.

Gut microbiome: From age-old hypothesis to multi-million-dollar industry

Which organ in the body weighs 2kg and is bigger than the average human brain? The gut microbiome may not be the first answer that springs to mind, but it has been named the 'forgotten organ' owing to its extensive influence on our physiology and pathology.

Back in the 20th century, Russian microbiologist and Nobel-Prize winner Élie Metchnikoff first spotted the potential to manipulate the intestinal microbiome with host-friendly bacteria found in yogurt, with a view to enhance health and delay aging. This age-old hypothesis has grown into a multi-million dollar industry since significant activity led Forbes to name the 2010s The Decade of the Microbiome. The global Human Microbiome Market was estimated to be worth $269 million in 2023 and is forecasted to reach $1.37 billion by 2029, growing at a CAGR of 31.1% in that time.

The gut microbiome's influence in our health

The four dominant phyla resident in the human gut are Firmicutes (which contains Lactobacilli), Bacteroidetes, Actinobacteria (which contains Bifidobacteria), and Proteobacteria. Human microbiota collaborate closely with the digestive tract to exert five predominant functions, namely to support digestive tract maturation, provide a barrier function against pathogens and toxins, and promote immune system development.

1. Promote digestion.
2. Support digestive tract maturation.
3. Provide a barrier function against pathogens and toxins.
4. Play a protective role in promoting immunity system development.
5. Support the synthesis of essential vitamins including Vitamin B.

The extensive genetic material encoded within the gut microbiome can synthesize enzymes with versatile metabolic capabilities and maintain important host functions, e.g., short-chain fatty acids, bile acids, tryptophan and indole derivatives, and neurotransmitters.

Any disturbances to the gut microbiome may trigger pathological processes such as digestive system diseases (e.g., inflammatory bowel disease), neurodegenerative and metabolic disorders, and cancer. More specifically, the gut and central nervous system are now known to communicate via the gut-brain axis (GBA); most gastrointestinal diseases result from altered transmission within the GBA that is influenced by both genetic and environmental factors. The GBA presents an attractive target for the development of novel therapeutics for an ever-growing list of disorders related to mental and digestive health.

Trends in gut microbiome research

CAS identified more than 250,000 scientific articles (mainly journal articles and patents) relating to gut/intestinal microbiome/microbiota, with nearly 15,000 being linked to various aspects of mental and gut health. Microbiome-related literature has sharply increased over the last decade, with steady exponential growth in journal articles from 1997 to 2022 (Figure 1). The number of patents grew rapidly until 2004, possibly correlating with the initial accumulation of knowledge and transfer into patentable applications. After that time, activity plateaued (Figure 1).

An examination of key publication concepts (approx. 4500 total) relevant to gut microbiome research in mental and gut health revealed “immunity” (>4000 documents) and “gut microbiome” (>3500 documents) as top concepts in the area. The “gut-brain relationship” concept exhibited the greatest growth rate between 2021 and 2022 (Figure 1).

Correlations were noted between gut microbiota and mental, metabolic, and digestive system disorders, cardiovascular and neurodegenerative diseases, various cancers, and immune and autoimmune diseases (Figure 2).

Dysbiosis, an imbalance in the microbiome structure that ultimately triggers pathological changes, was a particular trend noted in the publications analyzed, with other trending topics including depression, Alzheimer’s disease, Parkinson’s disease, and neurodegeneration (Figure 3).

Key players in the microbiome industry

A 2022 report estimates that >130 microbiome companies are evaluating >200 pipeline therapies in various stages of development. The top academic organizations responsible for journal publications were universities and research institutes, with the University College Cork, the Chinese Academy of Science, the University of California, and McMaster University leading the field.

Regarding patent activity, lead universities and medical centers included the University of California, and Johns Hopkins University, while Ares Medical and Merck are leading patent assignees (Figure 4).

Private investment is growing rapidly in microbiome research, which endorses the clinical potential of prebiotics, probiotics, and the gut microbiome overall. The average annual investment within this industry has risen from approximately $2 billion in 2014-2017 to just over $20 billion in 2021. The investment data clearly shows a recent and increasing commercial interest surrounding biotics and their potential in the therapeutic space.

Notable active investors include the French venture capital group Seventure Partners , U.S. life science innovators Flagship Pioneering, UK biotechnology firm Microbiotica, and Swedish probiotics firm Biogaia.

Clinical trial landscape for the treatment of mental health and digestive disorders

There are several notable completed and ongoing clinical trials investigating biotics in both digestive and mental health disorders (Table 1).

Table 1. Completed/ongoing trials investigating the use of biotics in digestive and mental health disorders.

The gut and beyond: The expanding potential of the microbiome

The past decade has seen a transformation in the way we regard our native bacteria and their impact on our health. There is extensive research activity investigating the use of microbiome therapies for the prevention and treatment of digestive and mental health disorders, with rising interest from pharmaceutical companies. We've seen substantial collaboration between biotechnology companies, academic institutions, and pharma. As such, we should anticipate further partnerships as research interests evolve. Beyond the gut-brain axis, secondary markets are emerging in areas such as dermatology, respiratory, oncology, and general lifestyle, suggesting that the manipulation of microbiota will soon become an intrinsic means of optimizing our health.

The gut microbiome is a vast and complex ecosystem that plays a critical role in a wide range of health conditions, including obesity, diabetes, inflammatory bowel disease, and even mental health disorders.

In collaboration with Bayer AG, this detailed landscape analysis reveals emerging trends in microbiome therapies, the wide range of diseases that could be impacted, and a deeper dive into the clinical pipeline, emerging players, and key influencers. Explore more below.

For the R&D leader who needs to stay ahead of the curve, this brief executive summary showcases why microplastics are such an alarming and potentially compounding problem that will have health, ecological, and pollution problems in the future. This highlights emerging trends and key takeaways for their respective teams to stay aware of key players, significant clinical advancements, and new approaches to solving the future of plastics pollution.

Fentanyl is a synthetic opioid designed for fast-acting pain relief up to 100 times more potent than morphine and 50 times more potent than heroin. Due to its relatively low cost, fentanyl is frequently mixed with other substances including heroin, cocaine, and methamphetamine. Yet even a small amount of fentanyl can be fatal, resulting in unintentional overdoses. Since 2015, fentanyl and its analogs have become the leading cause of drug-related fatalities in the United States.

Fentanyl has become a major public health crisis. According to the U.S. Congress Joint Economic Committee using the CDC’s methodology, the economic impact of the opioid crisis was an estimated $1.5 trillion in 2020. This includes treatment, prevention, and law enforcement. However, new scientific advances can help reduce future deaths by creating better pain relievers, reducing side effects, and developing vaccines.

The respiratory impact of fentanyl

At the most basic level, fentanyl works by binding to a group of opioid receptors, µOR, in the brain. These receptors are responsible for pain perception, mood, and breathing. When fentanyl binds to these receptors, it can cause several effects, including euphoria, confusion, and sedation, but the most dangerous ones are respiratory depression and arrest, unconsciousness, coma, and death.

The lethal dose of fentanyl (2mg) is so small that, in its powder form, the appearance is smaller than a pencil tip. Even more alarming is carfentanil (a dangerous analog derived from fentanyl), which is 100 times more powerful and only requires 0.02 mg (the equivalent of a few grains of salt) to be deadly.

*Lethal amounts can vary due to physiological make-up, duration of use, and additional substances 

Cheap and easily derived analogs create challenges

Fentanyl has a relatively low cost of production (~$1000 per kilogram) but it has a high street value of about $50,000-$110,000. This makes it a very profitable drug for criminals to sell. Fentanyl is also very easy to mix with other drugs, such as heroin and cocaine, which may make them more potent and addictive.

To avoid detection by authorities, drug dealers often synthesize fentanyl analogs. These drugs are chemically similar to fentanyl, but with slightly different structures. This makes them harder to identify and track.

More than 1,400 fentanyl analogs have been reported in scientific literature. This makes it very difficult for law enforcement to keep up with the latest trends in fentanyl production.

Fentanyl analogs are highly potent opioids that are often used in illicit drugs. There are about 42 analogs of fentanyl that are listed as controlled substances. These include alfentanil (CAS RN®. 71195-58-9), which is 600 times more potent than morphine, and carfentanil (CAS RN. 59708-52-0), which is 10,000 times more potent than morphine. Other common analogs of fentanyl that can lead to illicit drug overdose include acetylfentanyl, butyrfentanyl, and furanyl fentanyl. Carfentanil is responsible for the highest number of deaths.

Naloxone, today’s treatment for overdoses 

When fentanyl is ingested or transmitted, the best course of action to prevent an overdose is with naloxone, (commercially known as Narcan) which is now available in either injectable or nasal sprays under a variety of commercial names. This recently approved over-the-counter drug can rapidly reverse fentanyl or another opioid overdose. Typically, an overdose usually causes sedation, decreasing the respiratory rate and increasing respiratory acidosis (inability of lungs to expel carbon dioxide). Naloxone replaces fentanyl or its analogs by attaching to the same neurological receptors (i.e., µOR), reversing the effect of fentanyl in less than five minutes. Unlike morphine, a nearly 10-fold greater dose of naloxone is required to reverse the effects of fentanyl fully.

Future vaccinations could prevent overdoses 

1. Naloxone is a drug that can reverse opioid overdoses. However, it has two challenges:
2. It needs to be administered by someone who is aware that the victim is overdosing. It needs to be administered as soon as possible after the overdose.

Creating a vaccine could help prevent overdoses before they happen. Recently, scientists have made significant progress in developing vaccines for opioid use disorders. These vaccines are designed by coupling a hapten that resembles the targeted opioid structurally (fentanyl, morphine, etc.) with a carrier protein capable of eliciting an immunological response.

The antibodies produced by administering opioid-specific vaccines act by trapping the ingested opioid and preventing it from reaching the central nervous system (CNS) and other organs. This allows the body to avoid the activation of reward pathways and the development of dependence on the drug. A potentially attractive benefit of opioid vaccines is the longer duration of action conferred by antibodies over other opioid treatment options such as naltrexone depot injections, which could result in better patient compliance.

Monovalent and bivalent opioid vaccines against fentanyl, carfentanil, and combinations such as carfentanil/fentanyl, heroin/fentanyl, and heroin/oxycodone are being actively researched. These vaccines can be a more proactive solution that could be administered to at-risk individuals, healthcare workers, and first responders.

Reducing the side effects of future pain relievers 

In the United States, fentanyl is involved in more youth drug deaths than heroin, meth, cocaine, benzos, and Rx drugs combined. The development of safer opioids that reduce the risk of respiratory depression is critical. Advances in our understanding of the binding pockets, structural information, and downstream signaling of key opioid receptors may enable the development of safer opioids and reduce the unwanted side effects.

Binding pockets could reduce respiratory impact  

It has long been speculated that fentanyl and its analogs differ from morphine and other µOR agonists’ ability to recruit distinct downstream signaling molecules. This ability, referred to as biased signaling, was thought to be the reason behind the increased potency of adverse effects associated with fentanyl and its analogs.

Computational studies revealed that the flexible fentanyl molecule could assume a binding pose in the binding pocket of µOR that the rigid and bulky morphinan analogs could not. Historically, limited structural information about the interactions of fentanyl at µOR was available.

This recently changed when a group of researchers used cryo-electron microscopy to determine the structure of µOR bound to fentanyl and morphine (Figure 2B). The analysis of those structures revealed that fentanyl used a secondary binding pocket near the orthosteric site that morphine was unable to use. The ability of fentanyl to cause respiratory depression was shown to be linked to the conformational changes that it induced in µOR, allowing the recruitment of β-arrestin. β-arrestin is a signaling protein whose activation may lead to respiratory depression. 

Functional selectivity from downstream signals 

A growing body of research shows that different opioids can have different effects on the body, even though they act on the same receptor. This is called functional selectivity. For example, the opioid lofentanil is more likely to cause respiratory depression than the opioid mitragynine pseudoindoxyl. This is because lofentanil preferentially activates downstream signaling pathways that are involved in respiratory depression, while mitragynine pseudoindoxyl preferentially activates downstream signaling pathways that are involved in pain relief. This newly discovered information about functional selectivity can be used to develop new opioids that are more effective at pain relief and less likely to cause dangerous side effects.  

Looking ahead 

In recent years, there has been a growing focus on prevention efforts to reduce the number of opioid overdoses. These efforts include public education campaigns, naloxone distribution programs, and law enforcement initiatives to target the supply of opioids. The federal government has also invested heavily in prevention efforts. The White House’s National Drug Control Strategy (ONDCP) announced a $5 billion investment in increasing access to mental health care, preventing, and treating opioid addiction.

The high cost of opioid addiction and overdose is a major public health challenge, and more must be done to prevent these tragedies. Existing efforts to raise awareness of the dangers of fentanyl, make naloxone more widely available, and disrupt the flow of this drug into the United States can be accelerated by the emerging scientific breakthroughs that may lead to new drug formulations, fewer side effects, and vaccines for proactive protection.