What is the purpose of drug discovery?

Finding new drugs usually consists of five main stages: 1) a pre-discovery stage in which basic research is performed to try to understand the mechanisms leading to diseases and propose possible targets (e.g., proteins); 2) the drug discovery stage, during which scientists search for molecules (two main large families, small molecules and biologics) or other therapeutic strategies that interfere or cure the investigated disease or at least alleviate the symptoms; 3) the preclinical development stage that focuses on clarifying the mode of action of the drug candidates, investigates potential toxicity, validates efficacy on various in vitro and in vivo models, and starts evaluate formulation; 4) the clinical stage that investigates the drug candidate in humans; 5) the reviewing, approval and post-market monitoring stage during which the drug is approved or not. In practice, finding new treatments is very challenging. Despite advances in the understanding of biological systems and the development of cutting-edge technologies, the process is still long, costly with a high attrition rate. New approaches, such as artificial intelligence and novel in vitro technologies, are being used in an attempt to rationalize R&D and bring new drugs to patients faster, but several obstacles remain. Our hope is that one day, it becomes possible to rapidly design inexpensive, more specific, more effective, non-toxic, and personalized drugs. This is a goal towards which all authors of this article have devoted most of their careers.

Check now

GRAPHICAL ABSTRACT

Introduction

Drug discovery has a long history and dates back to the early days of human civilization. In those ancient times, treatments were often discovered by chance or resulted from observation of nature, typically but not exclusively, using ingredients extracted from plants/animals, and not just used for physical remedy but also for spiritual healing. Modern drug discovery research started to being performed around the early s. Nowadays, the development of a new medicine usually starts when basic research, often performed in academia, identifies a macromolecule (i.e., a molecule with a large molecular weight like genes/proteins), or a dysfunctional signaling pathway or a molecular mechanism apparently linked to a disease condition (pre-discovery stage) (Figure 1; Table 1) (Hefti, ; Hughes et al., ; Mohs and Greig, ; Villoutreix, ). In general, at this stage, research teams attempt to identify the so-called therapeutic targets (often a protein) that are linked to the disease state (Gashaw et al., ). To be nominated therapeutic target, scientists will also have to find therapeutic agents that modify the function of the perturbed target and restore health or alleviate symptoms. Finding the right target is however extremely challenging. Further, drugs are efficient in humans because of specific actions on the intended therapeutic target but also due to interactions with other, unintended (often unknown) targets! The process continues with the search of therapeutic agents followed by a preclinical phase, during which potential drugs are tested in a battery of animal models, to demonstrate safety and select drug candidates (novel strategies to avoid animal testing are being developed, see below). Clinical studies in humans can then get started to establish safety and efficacy of the drugs in patients with the highest benefit-to-risk ratio (Kandi and Vadakedath, ). The studies are then submitted to regulatory agencies, which review the documents and decide about market approval. If the review is positive, the drug can then be released to the market and be administrated to patients. Once a drug has been approved, investigations continue to monitor putative side effects that could be caused, over time, by the new treatment. This last step is often referred to as pharmacovigilance studies (or real-world evidence), generally dubbed &#;phase 4&#; clinical trial. The entire drug discovery and development process involves many disciplines, years of efforts and is very expensive. It also implies the generation and use of vast amount of data usually obtained via different types of high-throughput technologies. Many of these experiments and the analysis of the results can be automated via computer-assisted methods to speed-up some steps of the process, gain knowledge and reduce mistakes.

FIGURE 1

FIGURE 1. Drug discovery and development. The main stages are represented in a highly simplified manner. The process varies depending on the molecular mechanisms expected to be linked to the disease and the type of therapeutic agents that needs to be developed. The approximate cost is around US $2.8 billion and the time needed to complete the entire process is around 12&#;15 years.

TABLE 1

TABLE 1. Glossary.

As mentioned above, to act on a disease, the problematic target(s) have to be modulated by a therapeutic agent (or several). There is a wide variety of agents that traditionally fits into two major classes, the so-called &#;small molecules&#; (small chemical compounds, some modified short peptides&#;) and the &#;biologics&#; (typically macromolecules such as recombinant proteins, antibodies, siRNAs, long peptides, cells, genes &#; and vaccines). There are major differences between biologics and small molecules (Figure 2; Table 2) and we will essentially focus here on small molecules. It is also important to note that gene therapy is different from the other types of therapeutic agents because it is a technique that modifies a person&#;s genes to treat or cure a disease. In this case, the target is a disease-causing gene which has to be modified with a healthy copy of the gene, or the disease causing gene could be inactivated. Thus, beside technical issues, there are a number of ethical questions surrounding gene therapy and genome editing strategies that are not easy to answer. Further, some therapeutic agents are not acceptable to some parts of the population, as seen during the COVID-19 crisis and vaccine hesitancy. This is often due to misunderstanding of the biological processes and misinformation, resulting in fears, but yet this has to be considered. Also, about 5%&#;10% of the population are non-responders and have to receive other medications than vaccines. The division into small molecules and biologics is far from being perfect as some therapeutic agents combine a small molecule grafted onto a biologic (e.g., tisotumab vedotin is an antibody-drug conjugate used to treat cervical cancer). Therapeutic agents can be administrated to patients via different routes, called &#;routes of administration&#;. Small molecules can in general be administrated orally (the most convenient route for patients), while biologics usually need to be injected. The choice of a route of administration is also governed by the patient&#;s condition, for instance, in acute situations in hospitals, drugs are most often given intravenously. Other critical medical interventions that will not be discussed here are surgery, radiotherapy and psychological support.

FIGURE 2

FIGURE 2. Small molecules, peptides and biologics. The properties and sizes of the therapeutic agents vary greatly. Three molecules are presented at the same scale, these involve rivaroxaban, a small chemical molecule used to treat thrombosis and pulmonary embolism, cyclosporine, a short immunosuppressive cyclic peptide (11 amino-acids, a biologic that still resembles to a certain extent to a small molecule) used to treat post-transplant organ rejection and a biologic, pembrolizumab (antibody, over amino-acids), used to treat various types of cancer.

TABLE 2

TABLE 2. General characteristics of small molecule drugs and biologics.

Drug discovery and development: overview of the process

There are several stages in the drug discovery process that require numerous skills and the use of various advanced technological platforms (often a combination of computational and experimental approaches) to validate targets and search for therapeutic agents. When initial experimental compounds have been sufficiently optimized to be selective, potent and safe in preliminary in vitro experiments and animal models, they can be nominated as drug candidates. At this stage, the project focus shifts from drug discovery to drug development to enable human clinical trials. If the therapeutic agent is successful in all three phases of the clinical trials, it goes through regulatory registration and the drug can be marketed (Hefti, ; Hughes et al., ; Mohs and Greig, ).

Now, we will take a closer look at the process with the discovery of small molecules as an example. The process usually begins by focusing on a disease and the search of possible targets, often proteins, that can be modulated by small compounds (Hughes et al., ) (Figure 1). These compounds are expected to interfere or prevent the disease or at least limit the development of symptoms. These targets can be identified using cellular assays, genomic studies, proteomic studies, among many others. Then, thousands (to millions or even billions when using computer-aided drug design approaches prior to vitro assays) of small molecules have to be tested in various types of assays and a few promising molecules are then evaluated in animal models (and in alternative in vitro models) of human diseases. It is worth mentioning here that animal models can be misleading (e.g., a drug found toxic in animal models may not be toxic to humans or the opposite) (Pognan et al., ). At the same time, absorption, distribution and elimination studies (ADME) are conducted. After years of research, a few compounds will hopefully be safe and effective enough to take forward to trials in patients. The different stages can have different names in the scientific literature, often they are referred to as: the pre-discovery and basic research stage (around 5&#;6 years) in which targets and modifying small molecules are searched in silico (i.e., using a computer), in vitro (i.e., in the test tube), ex vivo (e.g., on tissues or organs) and in vivo using simple animal models (i.e., in a living organism, typically rats or mice) and a preclinical stage (2&#;3 years) during which the best small molecules are selected using various in silico, in vitro and in vivo experiments. In general, after all these steps, only a few compounds progress to the next stage. Toxicity is investigated further on at least two animal models [one rodent (e.g., rat) and one non-rodent (e.g., dogs, mini-pigs)] often using different administration routes before they become nominated clinical candidates and get a regulatory permission to proceed to human clinical trials. Prior to starting clinical trials, a so-called Investigational New Drug (IND) application is submitted to regulatory agencies (e.g., the Food and Drug Administration in the United States). Such documents, at least up to now (see below), usually include animal efficacy data and toxicity (Good Laboratory Practice (GLP)-compliant animal toxicology data are performed supporting the dose, dosing schedule, administration), manufacturing information, clinical protocols (e.g., patient population, number of patients, duration of the study) proposed for the clinical trials and information about the investigators of the study.

If the IND is approved, then clinical trials start (4&#;7 years) (Kandi and Vadakedath, ). In some specific cases such as cancer, a so-called phase 0 may get started, which involves the use of very small doses of the new drug in a limited number of people and sometimes in patients. This is an exploratory study with the goal of quickly exploring if and how the drug may work. In Phase I, the safety, and tolerability of the therapeutic agent (usually a single dose at first and then short-term multi-dose studies) is tested in a small number of healthy individuals (e.g., 20&#;80 people). Other parameters are investigated including the dose. Phase II typically involves 100&#;500 patients and the study can take place in several hospitals located in different countries. The study is designed to determine whether or not the therapeutic agent provides the desired therapeutic effect. Safety studies continue through the phase II trials. In the first part of phase II, referred to as phase IIa, the goal is to further refine the dose required to provide the desired therapeutic impact or monitored endpoints for the clinical candidate. Once the proper dose levels are determined, phase IIb studies can be initiated. The goal of the phase IIb is to determine the overall efficacy of the candidate drugs in a limited population of subjects. Numerous drug candidates fail in phase II due to safety issues or lack of efficacy. In phase III, the efficacy of the drug candidate is evaluated in a larger patient population. These studies are typically randomized and involve 1,000&#;5,000 patients at multiple clinical trial centers and are designed to determine the efficacy of the candidate compound relative to the current standard of care or a placebo, possible interactions with other medications and re-assess different doses (optimal dose is important for medication effectiveness). When neither the clinicians nor the patients know which of the treatments the patient is getting, the study is said to be double-blind. The cost and time associated with this phase can vary dramatically depending on the disease and the clinical endpoint under investigation. Phase III clinical trials are the most expensive part of drug discovery and development as it has a complex design and requires a large number of patients. Last but not least, formulation and stability studies are performed during the development stage to characterize the impurities present (either in batches or during storage conditions worldwide), and to determine the best formulation. Upon completion of the phase III trial, a New Drug Application (NDA) is submitted to the regulatory agencies to demonstrate drug safety and efficacy. Regulatory reviews can lead to requests for additional information, or even additional clinical trials to further establish either safety or efficacy. Ideally, these reviews lead to regulatory approval, including labelling requirements, and approval to market (review and approval &#;1&#;2 years). For approval, the drug must have adequate pharmaceutical quality, therapeutic effectiveness, and safety. It has to have a favorable &#;risk-benefit ratio&#;. Drugs offering important advances in treatment of a condition are given priority. Approval of regulatory bodies does not, however, signal the end of clinical trials. In many cases, regulatory agencies will require additional follow-up studies, often referred to as phase IV or post-marketing surveillance (&#;real-world evidence&#; trials) with infinite duration. In general, these studies are designed to detect rare adverse effects across a much larger population of patients or long-term adverse effects. The impact of phase IV studies can include alterations to labelling based on safety observations, contraindications for use of the new drug in combination with other medications, or even the withdrawal of marketing approval if the findings are severe enough.

Drug repurposing: challenges and opportunities

Drug repurposing or repositioning aims to take a drug (approved or in advanced clinical stages or even a drug that has been withdrawn from the market, most of the time it involves small molecules but biologics like antibodies are also explored), thus a molecule that has undergone extensive safety and efficacy testing, and use it for an additional or unrelated indication (van den Berg et al., ; Roessler et al., ; Schipper et al., ). In some situations, even a withdrawn drug can be repurposed like thalidomide, originally intended as a sedative and then used for treating a wide range of other conditions, including morning sickness in pregnant women. Thalidomide was then withdrawn due to causing birth defects but then was approved to treat leprosy (in ) and multiple myeloma (in ) (Begley et al., ). Drug repurposing approach can be very valuable in most cases including emergency situation like a pandemic, for rare and neglected diseases [for which specific drug developments are in general missing in pharmaceutical companies (Scherman and Fetro, ; Roessler et al., )]. This strategy is promoted as a cost- and time-effective approach for providing novel medicines. It is often claimed that repurposing drugs can be faster, more economical, less risky, and carry higher success rates as compared to traditional approaches, primarily because it is in theory possible to bypass early stages of development such as establishing drug safety. Other benefits that come with this approach include readily available products and manufacturing supply chains. Drug repurposing can be very profitable as in the case of fenfluramine (in , acquisition of Zogenix by UCB Pharma for about US$ 1.9 billion, https://www.ucb.com/stories-media/Press-Releases/article/UCB-Completes-Acquisition-of-Zogenix-Inc), a drug initially developed for weight loss, withdrawn and now used in several countries for the treatment of some forms of epilepsy (Odi et al., ). Yet, despite advantages, drug repurposing suffers from several issues. One problem is that there are no possibilities for optimization of the therapeutic molecule without losing the repurposing potential because any small change in the structure of the therapeutic agent means a new full manufacture process validation and preclinical safety development. Identifying an optimal dosage and formulation for the new disease indication can also be time consuming and requires novel investigations while side effects can indeed arise due to the new indication or in cases doses need to be changed. Also, assessing the patent status of the drug to repurpose requires very specific skills. The molecules that are investigated for repurposing are either patented or off-patent, and in some cases the intellectual property protection for the new indication may not be strong enough to engage in such project. Overall, while drug repurposing is intuitively attractive as it offers shorter routes to the clinic, challenges throughout the entire process are usually substantial. Investigating molecular mechanisms behind repurposing can however be very valuable as it can help identifying novel targets and as the repurposed drugs could be considered as starting point for the development of novel compounds (e.g., lenalidomide and pomalidomide are superior molecules derived from thalidomide) and as such emerge as breakthrough innovation in a reduced amount of time and still reduced cost compared to starting from scratch. It could also be of interest to combine several approved drugs (in some cases with a newer drug) to increase effectiveness.

Artificial intelligence: trust, but verify

Providing efficient and safe drug to patients is a long and complex process. The amount of data generated during this process or that can be collected from various sources is massive. It is thus necessary to integrate as much as possible quality data so as to be able to make decision in real time. Artificial Intelligence (AI or indeed, most of the time, machine learning) can definitely contribute here as it involves the use of powerful computers and efficient program algorithms to integrate large volume of data to train expert systems to perform a complex task (Brogi and Calderone, ; Ruffolo et al., ; Jayatunga et al., ; Sadybekov and Katritch, ). During the early discovery phases, AI is used to rationalize processes, and to assist in project management (e.g., definition of a target product profile that allows to locate each compound with regard to the expected final drug specifications in a complex multi-dimensional space), to summarize information, to understand better complex biological systems (e.g., using for instance system biology and chemogenomics approaches), or to propose original compounds or biologics (e.g., small molecules, peptides) generated by the machine under various types of constraints (e.g., ADMET constraints or affinity to the target) (Lambert, ; Gupta et al., ; Paul et al., ; Kontoyianni, ; Vijayan, et al., ). Most of the well-known success stories of AI have been in image recognition (e.g., in the early days, the approach was trained to for instance recognize cat and dog images, but today the method can be used to analyze biopsies or guide surgery) while also advertised in reducing time to reach phase I clinical trial. In the latter case, one can site the story of compound DSP-, developed by Exscientia and Sumitomo Dainippon Pharma, intended to treat obsessive compulsive disorder where time from first screening to the development stage was 4 time faster than using a conventional approach (although, unfortunately, the molecule failed in phase I, for numerous reasons including a difficult target while it was also observed that the molecules generated by AI were not novel) (Santa Maria Jr et al., ) (https://www.science.org/content/blog-post/another-ai-generated-drug; https://www.cas.org/resources/cas-insights/drug-discovery/ai-designed-drug-candidates). Similar observations have been posted by hundreds of financial analysts and research scientists about results obtained by other AI companies. In other words, the AI predictions are not perfect and indeed cannot be perfect at present (Bajorath, ; Bender and Cortés-Ciriano, ). This situation reflects the dependency of AI/machine learning to quality, size and diversity of the data used to train the mathematical models. There are millions of compounds (most will never be a drug) tested via standard experiments available in various databases, but there are only a few thousand approved in humans that are annotated on which to learn from, highlighting the so-called data gap (i.e., there are billions of pictures of dogs and cats to learn from, but a limited amount of quality data is available in the field of drug discovery despite the use of numerous the high-throughput approaches). The predictions can thus be misleading, because we do not have enough quality data as input and/or because we do not understand enough the complexity of the biological systems (Moingeon et al., ). During the drug development phases, in human, AI is associated to data-mining to for instance model some properties (e.g., PB/PK, PK/PD or population-based simulations and analysis, prediction of drug-drug interactions &#;). At this stage, these computer approaches can also be used to select the most informative population profile to be included in clinical trials or to explain the variability of effects, or provide « virtual » patients or populations, and applied to, for example, pediatric formulation using as input data collected on adults (Lang et al., ). Related to these, the concept of digital twins (which has been around for a while in other areas of research), now starts to be explored in the context of drug discovery and development. The overall idea would be to collect data about a particular disease, how it progresses, about the current treatments, about specific patients, and about a whole population, encapsulate all these data into a computer model so as to create a digital representation of a biological system or of a person and be able to simulate, for example, what might happen if one were to take a novel drug. While the concept is attractive, there are still major challenges and obstacles ahead but progresses are being made (An and Cockrell, ). Overall, AI, in the field of drug discovery and development, is still in the infancy stage and it will take time to fully integrate the technology into the R&D process (Hillisch et al., ). AI-discovered drugs do not guarantee success in clinical trials. The understanding of the data used as well as the critical mind of the scientists are key points that lead to the success or failure of AI-assisted drug research and development processes. The technology, in some circumstances, can make the process faster and more cost-effective, however, AI needs quality data to produce meaningful results and still today requires significant experimental validation. As such, it is important to trust AI, but verify the predictions (Schneider et al., ; Bajorath, ).

Rising cost: from drug discovery to new treatments

Analyses across all therapeutic areas indicate that the development of a new medicine, from target identification through approval for marketing, takes around 12&#;15 years and often longer. The cost to develop a new drug is very high, in part because failure is endemic in drug discovery, and success is rare. While various numbers have been reported, the latest formal assessment is around US $2.8 billion (DiMasi, ). There are many factors that contribute to this situation: the lack of understanding of what causes the disease can lead to the selection of the wrong therapeutic target; the impossibility of reaching the target with a sufficient concentration of drug in vivo without leading to adverse effects; no formulation compatible with the use of the drug in human; the therapeutic agent developed during years is found in phase III to have very low efficacy; the therapeutic agents or a metabolite (e.g., case of a small molecule) can interacts, specifically or not, with other drugs or with hundreds of molecules in the body, these interactions are usually not known in details and can lead to numerous adverse effects; animal experiments that are used to evaluate potency, selectivity, and toxicity during the different stages of the process can be highly misleading; stricter regulatory guidelines; duration of patents; the identified therapeutic molecule can be toxic in some patients but this could not be anticipated during the clinical trials due to the relatively small number of patients treated. Next and related to the cost of R&D, comes the cost of the treatments. Although there is a very complex protocol to determine the price tag of a drug (it varies from country to country, it can consider the insurance system, whether the drug is curative and represents a major advance to both patients and the health system or it has a minor effect on the disease), but in the end, biologics are generally much more expensive than small molecules, in part due to the complex manufacturing process. Studies suggest that on average, the daily dose of biologics costs 22 times more than a small molecule (Makurvet, ). It is important to keep in mind that the healthcare systems, in many countries, are about to collapse and that about half of the world population cannot get access to basic treatments (Ozawa et al., ). Biologics have been here for several decades already and are becoming increasingly important in several therapeutic areas. For example, cancer checkpoint inhibitors (e.g., the antibody ipilimumab and about 4&#;5 others at the time of writing) have received considerable and broad interest because of their ability to generate responses in many hitherto intractable malignant tumors. Yet, many recent studies suggest that such molecules lead to responses in less than 10%&#;15% of patients with cancer. Clearly, such molecules offer hope but also rise many questions (Fojo et al., ; Kantarjian and Rajkumar, ). That is, in some cases, biologics are real innovative breakthroughs, but in other situations, the strategy is pursued only for commercial reasons and alternative molecules such as small molecules are not even considered. These questions are, in theory, investigated by regulatory agencies [The United States Food and Drug Administration (FDA), European Medicines Agency (EMA), Pharmaceuticals and Medical Devices Agency (PMDA)] so as to try to avoid speculative drugs but more transparent processes would certainly be beneficial to patients and the general population. Although finding new treatments is very difficult, it is a profitable market, with global drug sales expected to grow to US$ 1.9 trillion by (Mullard, ).

Innovation in regulatory science and methodologies

It is important to note that, in step with the scientific progress in human tissue models research in the past decades, in the US, new medicines may not have to be tested in animals, according to legislation signed by the President Joe Biden in late December (&#;Text&#;S.&#;117th Congress (&#;): FDA Modernization Act 2.0.&#; 29 September . https://www.congress.gov/bill/117th-congress/senate-bill//text). Accordingly, US FDA is already accepting data from in vitro studies as part of the formal submission to the Agency (Wadman, ). Additionally, at the same time, following the leadership of some academic researchers (e.g., Guzelian et al., ; Hoffmann and Hartung, ), major European and US agencies started using evidence-based methodologies, such as systematic reviews and systematic maps, in toxicological assessment. These methodologies were developed and tested over the last 40 years in clinical research, spearheaded by Cochrane Collaboration (www.Cochrane.org) to compare the effectiveness of treatments, and have been applied to toxicological assessment of data-rich substances by the European Food Safety Authority (EFSA, ) and US Environmental Protection Agency&#;s (US EPA, https://cfpub.epa.gov/ncea/iris_drafts/recordisplay.cfm?deid=). While some of the aspects of these methodologies are not entirely applicable to drug-discovery because of the proprietary nature of the work, the main principles of evidence-based approaches, which encourage pre-publishing the methodologies before the research is conducted, comprehensiveness and transparency in data selection, minimization of bias (or systematic error), are in line with basic principles of the scientific method, and are applicable to drug discovery. Programs and drug candidates are all too frequently selected based on a biased opinion of a few scientists who are bound by similar training, scientific methodologies and beliefs. Opening-up drug discovery to scrutiny by other scientists with different training and opinions may lead to more failures in the earlier discovery stage, but less failures in the clinic, resulting in enhanced efficiency and more successes, benefiting the patients who need new treatments, first and foremost.

Concluding remarks

Drug discovery and development is a long and difficult endeavor; all novel ideas and strategies that can improve the process are valuable to explore. It is interesting to note that despite the steady increase in research and development expenditure, and major scientific advances in proteomics and genomics, the discovery of new drugs either seems to be drying-up some years or to remain essentially stable (Laermann-Nguyen and Backfisch, ). This situation has various origins (e.g., many diseases with no treatment are extremely difficult to study), while, certainly, industry scientists would benefit from greater exposure to new ideas from public research and public researchers would benefit from the private sector to move beyond exploration of molecular mechanisms towards the end goal of efficient development of candidate therapeutic agents. Along these lines, some countries like the United States and United Kingdom have been working extensively at improving academic drug discovery (e.g., all the skills and platforms connected via open research networks with rational protocols) but in the others, the process is fragmented (no coordination, no intent, duplication of efforts and inefficient investments &#;) and, thus, not capable of producing desired results compared to the time, energy and money spent. A first step could be to develop strong academic drug discovery networks in countries where this type of activity is not coordinated or not considered. Strong collaborations between the private sector, academia and not-for-profit institutions are clearly of major importance and have led to some successes in the past but such partnerships can be difficult to maintain over a long period of time (Yildirim et al., ; Takebe et al., ). The rationale being that open interconnections between the different scientific disciplines involved in drug research allow a &#;cross-fertilization&#;, each of them benefiting from the advances of the other fields. Obviously, such collaborations tend to be easier when academic and private research teams are located on the same campus, with possibilities of sharing ideas or technologies. Other types of collaboration imply building consortia, often for around 4&#;5 years, with research teams located in different cities or countries (unfortunately, most of the time, when the consortia have been built, they function as closed systems not allowing new scientists or novel research teams to join). Therefore, novel strategies need to be pursued, and among the novel public-private models that are being investigated, open science partnerships, could be of interest, if correctly implemented (e.g., the system must be open to all interested scientists, teams and relevant disciplines) (Gold and Edwards, ). Open science projects (Chodera et al., ), like the consortia models discussed above, are built on the differential expertise of the various partners, with generally academic and governmental partners taking on a larger role in the earlier stages and big pharmas leading in the later stages (e.g., advanced preclinical investigations, product development, manufacturing, and distribution). But in open science projects, results, publications, data, tools, and materials are open without regard for intellectual property. At some points, the various partners are free to use the results and develop their own proprietary products if deemed appropriate.

Next, novel technologies including AI could be a game changer in the years to come, even more so once we get past the hype stage. Novel approaches to replace animal models by more efficient, ethical, human-biology-based in vitro approaches could also play a significant role this next decade. Indeed, new tools and understanding, in, for instance, the area of investigative toxicology, are continually being implemented to reduce safety-related attrition in drug development (Aleo et al., ). Combining all these strategies, methods and know-how should definitively facilitate the design of more specific, effective, non-toxic, and patient-tailored drugs, thereby, providing a more optimistic outlook to the field. As a last note, we encourage the general public and patients to become more curious about the process of finding novel therapies, from the pre-discovery to the post-marketing stages. Further, crowd-funded citizen science initiatives are emerging in various areas of drug discovery and development (e.g.,https://www.clinicaltrialsarena.com/news/citizen-science-as-an-open-trials-tool-for-post-marketing-and-drug-repurposing--2/; see also the CTSA program at NIH), these projects are definitively valuable to the field.

Author contributions

NS, PV, and BV conceptualized the topic and drafted the first version of the manuscript. All authors contributed to the article and approved the submitted version.

Funding

Support from the INSERM institute is appreciated. This article is based upon work supported by the National Science Foundation under Grant No. .

Acknowledgments

We thank Stephane Auvin, Epileptologist and Child Neurologist, Head of the Pediatric Neurology Department, Robert Debré Hospital, for interesting discussions about the use of fenfluramine for the treatment of some forms of epilepsy.

Conflict of interest

Authors KT and BV were employed by the company Akttyva Therapeutics, Inc. Author NS is employed by Evotech Se.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher&#;s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

An, G., and Cockrell, C. (). Drug development digital twins for drug discovery, testing and repurposing: A schema for requirements and development. Front. Syst. Biol. 2, . doi:10./fsysb..

PubMed Abstract | CrossRef Full Text | Google Scholar

EFSA (). Protocol for a systematic review on health outcomes related to the age of introduction of complementary food for the scientific assessment of the appropriate age of introduction of complementary feeding into an infant's diet. EFSA J. 15, e. doi:10./j.efsa..

PubMed Abstract | CrossRef Full Text | Google Scholar

Fojo, T., Mailankody, S., and Lo, A. (). Unintended consequences of expensive cancer therapeutics&#;the pursuit of marginal indications and a me-too mentality that stifles innovation and creativity: The john conley lecture. JAMA Otolaryngol. Head. Neck Surg. 140, &#;. doi:10./jamaoto..

PubMed Abstract | CrossRef Full Text | Google Scholar

Gold, E. R., and Edwards, A. M. (). Overcoming market failures in pandemic drug discovery through open science: A Canadian solution. Front. Drug. Discov. 2. doi:10./fddsv..

Link to Lianhe Aigen

CrossRef Full Text | Google Scholar

Kantarjian, H., and Rajkumar, S. V. (). Why are cancer drugs so expensive in the United States, and what are the solutions? Mayo Clin. Proc. 90, 500&#;504. doi:10./j.mayocp..01.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Laermann-Nguyen, U., and Backfisch, M. (). Innovation crisis in the pharmaceutical industry? A survey. SN Bus. Econ. 1, 164. doi:10./s-021--5

CrossRef Full Text | Google Scholar

Lang, J., Vincent, L., Chenel, M., Ogungbenro, K., and Galetin, A. (). Impact of hepatic CYP3A4 ontogeny functions on drug-drug interaction risk in pediatric physiologically-based pharmacokinetic/pharmacodynamic modeling: Critical literature review and ivabradine case study. Clin. Pharmacol. Ther. 109, &#;. doi:10./cpt.

PubMed Abstract | CrossRef Full Text | Google Scholar

Pognan, F., Beilmann, M., Boonen, H. C. M., Czich, A., Dear, G., Hewitt, P., et al. (). The evolving role of investigative toxicology in the pharmaceutical industry. Nat. Rev. Drug. Discov. 1, 317&#;335. doi:10./s-022--x

CrossRef Full Text | Google Scholar

Santa Maria, J. P., Wang, Y., and Camargo, L. M. (). Perspective on the challenges and opportunities of accelerating drug discovery with artificial intelligence. Front. Bioinform. 3, . doi:10./fbinf..

PubMed Abstract | CrossRef Full Text | Google Scholar

Takebe, T., Imai, R., and Ono, S. (). The current status of drug discovery and development as originated in United States academia: The influence of industrial and academic collaboration on drug discovery and development. Clin. Transl. Sci. 11, 597&#;606. doi:10./cts.

PubMed Abstract | CrossRef Full Text | Google Scholar

Abstract

This article provides a brief overview of the processes of drug discovery and development. Our aim is to help scientists whose research may be relevant to drug discovery and/or development to frame their research report in a way that appropriately places their findings within the drug discovery and development process and thereby support effective translation of preclinical research to humans. One overall theme of our article is that the process is sufficiently long, complex, and expensive so that many biological targets must be considered for every new medicine eventually approved for clinical use and new research tools may be needed to investigate each new target. Studies that contribute to solving any of the many scientific and operational issues involved in the development process can improve the efficiency of the process. An awareness of these issues allows the early implementation of measures to increase the opportunity for success. As editors of the journal, we encourage submission of research reports that provide data relevant to the issues presented.

1. The process: Many years, many failures, much uncertainty

Most often, the development of a new medicine starts when basic scientists learn of a biological target (e.g., a receptor, enzyme, protein, gene, etc.) that is involved in a biological process thought to be dysfunctional in patients with a disease such as Alzheimer's disease (AD). Here, we are considering the discovery and development of entirely new medicines, those with a mode of action different from already approved medicines and intended for a clinical indication that is not addressed by approved medicines. Better medicines that are iterative improvements on current medications are valuable as they may offer benefits over existing medications in terms of potency, safety, tolerability, or convenience, but they usually do not involve the manipulation of biological targets different from those directly affected by existing medications.

Analyses across all therapeutic areas indicate that the development of a new medicine, from target identification through approval for marketing, takes over 12 years and often much longer [1]. The cost to develop a New Molecular Entity (NME; a small molecule compound) or New Biological Entity (NBE; an antibody, protein, gene therapy, or other biological medicine) is certainly over $1 billion and, on average, has been estimated to be about $2.6 billion [2]. Fig. 1, adapted from Paul et al. [3], shows a schematic of the stages involved in developing a new medicine along with average times required for each stage and the approximate cost (in dollars) for each phase of development. Importantly, Fig. 1 also depicts the number of molecules that must be entered into each stage of development to, eventually, produce one new approved medicine. This figure is based on analyses across several therapeutic areas and includes data from development programs that are new iterations of existing medicines as well as those seeking medicines based on completely new targets or that aim for completely unprecedented therapeutic indications. It seems highly likely that the numbers in Fig. 1 greatly underestimate the numbers of molecules needed at each stage of development to produce a new medicine to treat a disease for which no therapy currently exists. Separate figures for AD drug development programs are not available, but the last line of Fig. 1 shows clinical transition probabilities calculated by Cummings et al. [4], who reviewed all of the 244 unique compounds studied in clinical trials for AD from through . It is evident that the likelihood of advancing an AD drug candidate has been very low when compared with those for development programs across a broad range of therapeutic areas. Stated another way, the probability is very low that any new biological target or molecule identified as potentially relevant to the modification of AD will result in an approved new medicine. We should anticipate that a very large number of biological targets will need to be discovered and interrogated pharmacologically and genetically to achieve a single new disease-modifying medicine for AD.

Fig. 1.

A diagram of the stages of drug discovery and development with estimates of cost and duration.

Adapted from [3] and [4].

In accordance with this, central nervous system (CNS) drugs have lower success rates and take a longer time to develop, than do other drug classes. Specifically, the success rate of neuropsychiatric drug candidates who enter into human testing to effectively reach the marketplace is dramatically lower (8.2%) than for all drugs combined (15%) [5], [6]. In the case of drugs focused toward AD progression, of the numerous evaluated clinically, the attrition rate has thus far been 100%. Furthermore, the average clinical development time for neuropsychiatric drugs is in the order of 8.7 years, as compared with 5.9 years for antiviral agents, almost 50% longer. The time required to gain regulatory approval is also longer for neurological drugs, 1.9 years as opposed to an average of 1.2 years for all drugs. Taking into account the approximately 6 to 10 years that drugs generally are in the preclinical phase of development, neurological drugs can take up to 18 years to run the gauntlet from initial laboratory evaluation to regulatory approval and use [5], [6]&#;a long duration in relation to the current 20-year patent protection rights. The drug development process is set up, particularly at the stage of clinical development, to &#;fail fast, fail early&#; in a strategy to eliminate key risks before making a expensive late-stage investment [7], [8]. Nevertheless, neurological agents tend to fail later during the clinical development process&#;in phase 3 clinical trials [5], [6], particularly for recent AD experimental therapeutics, thereby making CNS drugs among the most expensive to develop. It is hence important to optimize each piece of the preclinical and clinical development process.

2. Discovery: From target to clinical candidate

The goal of a preclinical drug discovery program is to deliver one or more clinical candidate molecules, each of which has sufficient evidence of biologic activity at a target relevant to a disease as well as sufficient safety and drug-like properties so that it can be entered into human testing. Most discovery programs seek to produce more than one candidate molecule because, as is shown in Fig. 1, many molecules do not move through the entire process because of problems with safety, kinetics, potency, intellectual property protection, or other factors. There is no simple formula for producing a viable clinical candidate molecule, although extensive collaboration of chemistry, biology, toxicology, and pharmacokinetics is almost universally the norm in modern drug discovery programs [9]; small molecule drug discovery programs typically produce massive amounts of data using high-throughput screening techniques that evaluate many compounds at many doses against many assays [9].

Some of the information that should be developed during discovery studies for a clinical candidate molecule is shown in Fig. 2. All of the topics listed in this figure will need to be addressed before deciding whether a molecule is suitable for testing in humans. There are no perfect discovery programs, and some of the desired information listed in Fig. 2 may be missing; however, gaps in knowledge at this stage often lead to difficulties in interpreting later studies. Critical to moving any molecule forward will be an assessment of target validity; that is, does the molecule target an aspect of biology that is relevant to the disease of interest? And, is the target expressed in the human brain during the disease process that allows a window of opportunity for treatment? Target validation has no uniformly accepted definition, although data from humans showing some relationship of the proposed target to the disease, such as AD, are essential. For potential medicines that are designed to be improved iterations on already approved medicines, the validating data are usually quite compelling and derive from the fact that other medicines with similar mechanism of action have shown efficacy. For AD, where there is no disease-modifying medicine, such validation is not available. In the search for medicines directed toward completely novel targets, advances in genetics, such as the Human Genome Project, have produced many potential new pharmacological targets and genetic &#;validation&#; is often cited as a reason for pursuing a novel drug target [10]. However, mechanistic targets such as receptors and enzymes that are well understood biologically have led to many of the medicines currently used; in addition, whole animal models that reproduce some physiological aspects of human disease such as abnormal activity in a specific neural circuit have also been used successfully [10]. None of these approaches to target validation are a guarantee of success in screening for potential new medicines, but it is important to be very explicit about the data supporting the pursuit of a target and the kinds of screening tools available for identifying potential clinical candidates. This explicit understanding will help insure that results obtained with one molecule can be used to help inform the development program for the next molecule.

Fig. 2.

A summary of the information to be developed before the selection of a clinical candidate molecule proposed for testing in humans.

Drug developers seeking medicines for diseases, such as AD, cancer, or other difficult to treat diseases, are very eager to learn about new targets that might be the focus of a new drug discovery program. At the same time, they are often very skeptical of new findings in the scientific literature that claim to have identified a NBE or process that could be a target of interest. The investment in time and money needed to pursue a new biological target is very large so that drug developers nearly always attempt to reproduce reported findings before engaging in screening against novel targets; such efforts to reproduce even findings reported in the most reputable journals find that most, maybe even as many as 90%, cannot be reproduced [11]. The reasons for this high failure rate are a matter of some discussion; for present purposes, it is important to note that investigators proposing that a new finding is relevant to drug discovery should expect a high level of skepticism and, even if industry investigators show interest, it is almost certain that there will be attempts to replicate the findings and explore their reproducibility with other animal models, cell lines, and physiological conditions [12].

3. Development: Kinetics, drug disposition, safety, biomarkers, and efficacy

A biological target, even one with validating data, will only be useful for drug development if it is possible to make molecules that affect the target in a way that could be well tolerated and therapeutically useful. Furthermore, those molecules must be shown to have properties enabling them to act like a medicine when given to people. The molecules must have pharmacokinetic (PK) properties that enable there to be a predictable and consistent relationship between the dose of the drug given, exposure of the drug at the proposed site of action, and the binding of the drug to the target of therapeutic interest. The preclinical and later clinical studies needed to determine these PK properties of a proposed medicine are extensive [13] and are particularly complex for CNS targets because of the blood-brain barrier. Fortunately, advances in medicinal chemistry and biological PK modeling have reduced the number of molecules entering clinical development with unsatisfactory PK properties [13]. Indeed, this represents an area in which the identification of a problem, unsatisfactory PK/bioavailability, has resulted in implementation of effective strategies to remedy a significant cause of drug development failures. During , unsatisfactory PK/bioavailability properties of experimental drugs represented the most significant cause of attrition, accounting for approximately 40% of drug development failures. By , however, this cause of attrition had fallen to less than 10% [14]. The appreciation that previously successful drugs typically have physicochemical and structural properties within certain ranges, and the application of this knowledge when considering the synthesis of new chemical entities, as proposed by Chis Lipinski in his &#;rule of five&#; [15], has positively impacted the development of both systemic and CNS drugs.

The range of potentially safe and tolerable doses of a molecule must be determined before human testing. Toxicology studies in at least two nonhuman species are usually used to determine a projected safe dose range and to provide information about compound distribution, organ-specific toxicity, and metabolism [16]. These studies should provide information on the emergence of adverse effects as compound dose is increased and provide guidance on compound-specific monitoring that might be needed in early clinical studies. Serious, irreversible adverse effects observed in these studies within some multiples of the projected efficacious dose are likely to prevent further development of the compound. Compound failure rates due to toxicity before human testing are relatively high [7], and account for as much as 30% of drug attrition occurring during the clinical stage of development [14], emphasizing the need to have backup compounds for targets that are well validated and of high strategic importance. The other key cause of attrition is a lack of efficacy, accounting for some 30% of drug development failures [14] and quite possibly more for CNS drugs in which animal models of efficacy are indisputably unpredictive&#;likely consequent to the complexity of the human brain in comparison with rodent animal models and the complex etiology of neurological disorders.

As indicated in Fig. 1, only about 1 in 8 compounds entering clinical development in the pharmaceutical industry is eventually approved for marketing. As noted, the success rate is much lower for diseases such as AD. The recent review mentioned previously [4] found that 244 compounds entered clinical development for AD between and with only one of them (memantine&#;an N-methyl-D-aspartate receptor antagonist and symptomatic, rather than disease progression, drug) achieving regulatory approval; this is a failure rate of 99.6%. Even with the extraordinarily high failure rate in this disease, companies have continued to invest because the unmet medical need is great and there are scientifically plausible targets to pursue. Most (78%) of the 413 clinical trials (244 unique molecules) conducted of potential medicines for AD between and were supported by the pharmaceutical industry [4].

While investment in AD therapeutics continues, the high cost of failures means that scientists proposing new targets or molecules for development should examine carefully the characteristics associated with successful development programs. In an indication where failure is the norm, it is far better if those failures occur earlier rather than late in development. Also, it is best if each study in the development program yields data that provide a compelling basis for termination, continuation, or specific modification of the compound development program; data that are difficult to interpret scientifically can lead to more studies and delayed decision-making with no prospect for better studies in the future. Even programs that are terminated can be &#;good failures&#; if the termination has convincingly tested a proposed therapeutic mechanism, a soundly based scientific hypothesis, or provides clear direction for future studies [7]. Some key elements needed for highly informative phase 2 programs have been identified [17] and are summarized in Fig. 3. The first essential element is clear evidence that the molecule being tested has adequate exposure at the proposed site of action. For AD therapeutics, this usually means somewhere in brain; CSF studies in humans are often used as indirect measures of CNS target exposure. The second key element is evidence for binding to the therapeutic target, such as a receptor, enzyme, protein, or specific brain structure. Brain imaging technology, particularly positron emission tomography, has enabled target engagement studies for many CNS molecules; such studies may also help inform the most likely clinically efficacious dose [18]. The third essential element for success in phase 2 is evidence for a pharmacodynamic (PD) or downstream biological effect of the drug&#;best associated with its proposed mechanism of action or underpinning the scientific hypothesis being tested. Measures of amyloid β concentrations in blood and CSF have provided useful measures of the PD effects of amyloid β lowering compounds such as the γ- and β-secretase inhibitors [19]. The recent development of techniques to sample the contents of extracellular vesicles (exosomes) enriched for neuronal origin from peripheral blood and to use them as a biomarker discovery platform for neurological disorders [20] has the potential to provide a window into disease progression within the brain and its response to drugs. Drug development scientists in the pharmaceutical industry often look to academic laboratories for leadership and partnership in developing the tools needed to assess PD effects of drugs, as the technology for making these assessments (e.g. biochemical assays, imaging procedures, positron emission tomography ligands, evaluating the cargo of exosomes) is often not compound specific and may require scientific knowledge not readily available in a company.

Fig. 3.

A summary of the critical compound-related information needed from phase 2 to improve the likelihood of moving to phase 3.

Adapted from [12].

Clear measures of exposure, target engagement, and downstream biological effects do not assure clinical efficacy, but they do insure that the studies in the clinical development program are testing the therapeutic value of a proposed biological target and intervention strategy. The viability of a new biological finding as a therapeutic target can be markedly enhanced by technologies that enable measurement of exposure, target engagement, and PD effects. If such measures are available, the proposed target will have a much better chance of being followed up with a well-funded discovery and development program. When such measures are ignored, it is difficult to interpret data showing that a drug is ineffective because of flaws in the drug development and design process (e.g., inadequate exposure of the target to the drug), rather from lack of efficacy. In the former case that can potentially be remedied, the proposed mechanism of action and/or scientific hypothesis has not been evaluated under appropriate conditions optimized to expect drug action; resulting in a type 2 error and the waste of resources [21].

4. Essential elements: Linking and cross-validating studies as one progresses through the preclinical/clinical process, early proof of mechanism studies, check lists to help avoid hidden errors

Collaboration across disciplines and between preclinical and clinical studies is almost essential for a successful drug discovery and development program. Activities contributing to the eventual approval and use of new medicines come from academic laboratories, large and small pharmaceutical companies, and multiple contract research organizations. Fig. 4 lists many of the kinds of research activities that are needed to produce a new medicine; to be done effectively, they should be done with some recognition of how these pieces fit together and with the recognition that the results from one activity must be communicated effectively to scientists involved in each of the other activities. No single type of research provides the entire key to success.

Fig. 4.

A partial list of the kinds of data and research tools that contribute to a successful drug discovery and development program.

Linking and cross-validating studies, whether undertaken within the same laboratory or across laboratories, is an essential component to a successful drug development program&#;particularly when such studies relate to the mechanism of action of the experimental drug of interest and/or fundamental hypothesis being evaluated. All too often drug development uses approaches that are intended to minimize the time to regulatory approval and focus too exclusively on obtaining evidence of drug efficacy, rather than understanding drug action and testing the founding hypothesis. Evaluations of regulatory efficacy measures alone, albeit necessary steps to successfully develop drugs, are too frequently insufficient within themselves to support the scientifically rigorous translation of discoveries into clinical practice benefits and advances in scientific knowledge and methods. The simultaneous evaluation of measures associated with drug mechanism/hypothesis can open new avenues of research as well as close them, and advance our knowledge of the brain and its targets for intervention. To ensure against errors, often hidden ones, a &#;check list&#; is valuable (indeed, crucial) to optimize the translation of a drug candidate through the nonclinical and clinical stages of the drug development process and, thereby, maximize the potential for success [22]. The publication of positive and negative drug development studies and clinical trials, likewise, is essential as when such information goes unreported, the predictive value of preclinical models cannot be evaluated, and investigators cannot not learn from earlier failures how to improve methods and practices [23], [24].

5. Additional uncertainty: Regulations, manufacturing, finding the right patients

The discovery, design, and synthesis of one or more good molecules directed toward validated targets and informed by well-designed and well-executed clinical and nonclinical studies will eventually lead to a submission package for regulatory approval. In planning a development program, it is important to determine whether the proposed studies will satisfy regulatory requirements for evaluating safety and efficacy and enable development of an informative label for the new medicine. Although few medicines have been approved for AD, regulators in the United States and other countries have been very proactive in developing regulatory guidance on new medicines in this area [25]. However, it is possible that a proposed new medicine is envisioned to work in a way that is not covered by published regulatory guidance; in such a case, the developers must work interactively with regulatory officials to come up with a plan that does justice to the mechanism proposed and that will satisfy regulatory needs.

Once a molecule is approved, it must be manufactured according to high standards of purity and stability as prescribed by regulations [26]. Although manufacturing is not usually a concern for discovery biologists, the process of manufacturing a new medicine can be complex and expensive, particularly for biological products (NBEs). The complexity of manufacturing may play a role in determining the financial viability of investment in a specific biological target. Finally, a new medicine must find acceptance in the medical community so that physicians and patients can be assured that the medicine can be given to the right patients, at the right doses at the right time. This is essentially what is meant by the term &#;personalized medicine&#; [27]. For new medicines designed to be used in a group of patients already identified by widely used diagnostic practices, finding the right patients may not be too difficult. If the appropriate patient population is not one identified by current diagnostic practice, then efforts must be taken to enable clinicians to identify the right patients before product launch. Recent studies in AD therapeutics geared toward prodromal and preclinical AD, if successful, require new diagnostic approaches in clinical practice [28].

6. If at first you don't succeed (and you won't), learn, then try again

Fig. 5 provides a schematic of many of the activities that occur during the drug discovery and development process. Note that many molecules, both NMEs and NBEs are considered in the discovery phase at the left to yield a single approved medicine. The flow of new information from basic science, through preclinical and clinical development, to Food and Drug Administration filing along with critical activities at each stage is depicted. Also critical, however, are the reverse arrows at the bottom showing that later preclinical and clinical data identifying deficiencies (failures) in some molecular approaches provide feedback to inform the conduct of studies at earlier stages of discovery and development that are more likely to yield successful medicines. With long time lines for drug discovery and development, and high failure rates, many investigators will work in a therapeutic area such as AD for years without seeing a new generation of medicines. This can be frustrating but other therapeutic areas such as cancer and heart disease have also gone through long periods of incremental scientific advances before the introduction of markedly more effective therapeutic agents. Analyses that take the long-term view of the drug development process indicate that incremental learning shared across the various disciplines involved in the process and across the stages of drug development is key to the delivery of new medicines [29]. Sharing of precompetitive data and clinical trial results by commercial sponsors is also important for the advance of science and contributes toward the more rapid introduction of new medicines [29], [30]. Incentives that provide intellectual property protections for investment in expensive, high-risk research directed toward areas of high unmet medical need can encourage both investment and data sharing by commercial sponsors [31]. In spite of the lack of new AD medicines in recent years, the drug discovery and development process has improved as a result of more precise diagnosis, information on natural history of AD and measurement of clinical progression, better biomarkers, genetic findings, and knowledge of pathophysiology. Such knowledge, combined with a focus on good molecules and efficient development plans will ultimately produce better medicines.

Fig. 5.

A schematic of the activities involved in the drug discovery and development process. At the left are shown icons depicting small molecules (NMEs) and biological molecules (NBEs) being considered for development. At the top are the time lines for quality assurance guides governing the process; they are good laboratory practice (GLP), good manufacturing practice (GMP), and good clinical practice (GCP). Specific activities in the stages of development are shown at the bottom; they include studies of absorption, distribution, excretion and metabolism (ADEM), screening for activity at cytochrome P450 (CYP) liver enzymes, and regulatory filings for Investigational New Drug (IND) and New Drug Application (NDA). Abbreviations: NBE, New Biological Entity; NME, New Molecular Entity.

Acknowledgments

R.C.M. is an employee of the Global Alzheimer's Platform (GAP) Foundation, a nonprofit organization; previously, he was an employee of Eli Lilly and Company and still holds stock in Lilly. He is also the vice president for Clinical Development at AgeneBio, Inc. N.H.G. is an employee of and supported by the Intramural Research Program of the National Institute on Aging, National Institutes of Health, and has no disclosures to report. Christian Felder, Ph.D., Research Fellow at Eli Lilly and Company provided critical comments and suggestions on an earlier version of this manuscript.

Footnotes

The authors have declared that no conflict of interest exists.

Are you interested in learning more about Drug Discovery? Contact us today to secure an expert consultation!