The Evolution of Chemical Biology into Translational Physiology and Precision Medicine

Abstract

Pharmaceutical research has undergone significant transformation over time, particularly in the development of potent compounds that target specific physiological mechanisms. The need to demonstrate clinical benefit posed challenges. These challenges led to the rise of translational physiology and precision medicine aided by the development of the chemical biology platform. The chemical biology platform is an organizational approach to optimize drug target identification and validation and improve safety and efficacy of biopharmaceuticals. The platform achieves this goal through emphasis on understanding the underlying biological processes and leveraging knowledge gained from the action of similar molecules on these biological processes. The platform connects a series of strategic steps to determine whether a newly developed compound could translate into clinical benefit using translational physiology. Translational physiology examines biological functions across multiple levels, from molecular interactions to population-wide effects, and has been deeply influenced by the advancement of the chemical biology platform. Unlike traditional trial-and-error methods, by leveraging systems biology techniques, such as proteomics, metabolomics and transcriptomics, chemical biology prioritizes targeted selection to enhance drug discovery. This historical review explores the evolution of the chemical biology platform and its role in precision medicine, highlighting its continued influence in both academic research and pharmaceutical innovation. By fostering a mechanism-based approach to clinical advancement, chemical biology remains a critical component in modern drug development. Additionally, understanding the history and integrative nature of this platform is essential for training the next generation of researchers in the design of experimental studies that effectively incorporate translational physiology.

Introduction

The last 25 years of the 20th century marked a pivotal period in pharmaceutical research and development. While pharmaceutical companies began to produce highly potent compounds targeting specific biological mechanisms, they faced a significant obstacle: demonstrating clinical benefit. The challenge of demonstrating clinical benefit paved the way for transformative changes in drug development, leading to the emergence of translational physiology and personalized medicine, later termed precision medicine.(1) Translational physiology is defined as the examination of biological functions across levels spanning from molecules to cells to organs to populations. The Translational Physiology Interest Group began in the American Physiological Society in 2010, when the first author of this perspective (MLL) became its inaugural chair. A critical component in the transition to translational physiology was the development of the chemical biology platform.

The Role of Chemical Biology in Drug Development

Chemical biology refers to the study and modulation of biological systems, and the creation of biological response profiles. This is achieved through the use of small molecules that are often selected or designed based on current knowledge of the structure, function, or physiology of biological targets.(1) While traditional approaches relied primarily on trial and error, including when using high throughput technologies, chemical biology focuses on selecting target families and can incorporate systems biology approaches to understand how protein networks integrate. The main advantage of incorporating a chemical biology platform into strategies to develop novel therapeutics is that it uses a multidisciplinary team to accumulate knowledge and to solve problems, often relying on parallel processes to speed up the time and reduce the costs to bring new drugs to patients. Unlike traditional trial-and-error methods, chemical biology emphasizes targeted selection and integrates systems biology approaches (for example, transcriptomics, proteomics, metabolomics, network analyses) to understand protein network interactions.

This historical review explores the evolution of the chemical biology platform and the concepts of translational physiology and precision medicine to provide a roadmap on how to design mechanistic studies that incorporate translational physiology. This evolution was stimulated by the introduction of biomarkers and fostered by the development of the chemical biology platform.(1) We highlight the positive impact of using a chemical biology platform approach to both enhance drug discovery and drive the transition to precision medicine.(2) This approach persists in both academic and industry focused research as a mechanism-based means to advance clinical medicine. Understanding the history of the chemical biology platform is crucial for physiology educators, as it helps them train the next generation of physiology researchers in experimental design. Since physiology forms the core of this platform, instilling an appreciation for its integrative role will better equip students to conduct research. Physiology forms the core of the chemical biology platform by providing essential biological context in which chemical tools and principles are applied to understand and influence living systems. Physiology allows us to understand function in a living context, identify and validate targets, bridge molecular and systems biology, discover drugs for therapeutic use, and develop tools for directed application (Figure 1). This ensures that chemical tools and insights are biologically meaningful, relevant, and translatable to real-world health and disease contexts.

Figure 1.

Physiology forms the Core of the Chemical Biology Platform. The central circle labeled Physiology is surrounded by five interconnected domains: Biological Context for Chemical Tools; Target Identification and Validation; Linking Molecular to Systems Biology; Drug Discovery and Therapeutics; and Tool Development and Application. The arrows indicate the dynamic and reciprocal interactions between physiology and each domain, emphasizing its foundational role in guiding chemical biology research and applications. Created in BioRender, https://BioRender.com/api85ka.

Steps to developing the chemical biology platform

Bridging disciplines between chemists and pharmacologists was the first step. Prior to the 1950s-60s, pharmaceutical scientists primarily included chemists and pharmacologists. Chemists extracted, synthesized, and modified potential therapeutic agents and worked on syntheses to scale up production. Pharmacologists used animal models and later added cell and tissue physiology systems to show potential therapeutic benefit, determine dosage, and develop absorption, distribution, metabolism, and excretion (ADME) profiles.(3) Some pharmacologists specialized in toxicology. Later some companies began to separate the pharmacologists into biologists/physiologists who focused on therapeutic benefit and pharmacologists who focused on ADME. Note that pharmaceutical companies preferred to use the term biologists instead of physiologists. The Kefauver-Harris Amendment in 1962, in reaction to the thalidomide birth defects, demanded among other things, proof of efficacy from adequate and well-controlled clinical trials.(4) This divided Phase II clinical evaluation into two components: Phase IIa focused on finding a potential disease in which the potential drug would work and Phase IIb and Phase III focused on demonstrating statistical proof of efficacy and safety. Phase III is generally a much larger and more refined clinical trial based on information gleaned from the Phase IIb trial.

The second step was the introduction of clinical biology to bridge relationships and foster teamwork. The concept of clinical biology emerged to encourage collaboration among preclinical physiologists and pharmacologists and clinical pharmacologists. Clinical biology refers to the use of laboratory assessments (later termed biomarkers) to diagnose disease, evaluate patient health, and monitor treatment efficacy. Interdisciplinary teams focused on identifying human disease models and biomarkers that could more easily demonstrate the effect of a drug before progressing to costly Phase IIb and III trials. The Clinical Biology team was tasked with identifying human models of disease in which one could demonstrate the effect of the potential drug on the biomarker and look for accompanying evidence of clinical efficacy in a small number of patients. Clinical Biology, therefore, encompassed Phases I and IIa. By having physiologists and pharmacologists collaborate closely to identify appropriate biomarkers and models of human disease in which to test proof of concept, this increased the efficiency and effectiveness of making decisions before the company launched time consuming and costly Phase IIb and Phase III trials. To replicate human disease, a model should possess the biomarker of interest, have clinical symptoms that are easily monitored, and demonstrate a relationship between biomarker concentration and clinical symptoms of the condition.

By the early 1980’s, advances in molecular biology and biochemistry provided a means to identify and target specific DNA, RNA, and proteins involved in disease processes. For example, the protocol for immunoblotting was first developed in the late 1970’s and early 1980’s and allowed relative quantitation of protein abundance. Similar approaches were in place for analysis of DNA and RNA. While the pharmaceutical industry now had tools needed to assess potency and selectivity of compounds developed for therapeutic management of disease, the challenge remained: how to effectively demonstrate clinical benefit in patients. This gap between laboratory success and clinical efficacy prompted a re-evaluation of drug development strategies.

During this time, the senior author (FLD) joined Ciba (now Novartis) and in 1984, the Clinical Biology department was established at Ciba to bridge the gap between preclinical findings and clinical outcomes. Douglas identified four key steps, based on Koch’s postulates, to indicate potential clinical benefits of new agents: 1) Identify a disease parameter (biomarker); 2) Show that the drug modifies that parameter in an animal model; 3) Show that the drug modifies the parameter in a human disease model; and 4) Demonstrate a dose-dependent clinical benefit that correlated with similar change in direction of the biomarker.

This approach led to termination in development of CGS 13080, a Ciba Geigy thromboxane synthase inhibitor, due to early uncovering of a lack of feasibility of an oral formulation that limited use.(5, 6) While intravenous administration of CGS 13080 demonstrated a decrease of thromboxane B2 (the metabolite of thromboxane A2) and clinical efficacy, for example in reducing pulmonary vascular resistance for patients undergoing mitral valve replacement surgery, the half-life of CGS 13080 was very short at 73 minutes and oral formulation was not feasible to achieve treatment success.(7–9) Other companies, including Smith Kline, Merck, and Glaxo Welcome similarly terminated their thromboxane synthase inhibitors and receptor antagonist programs.

Clinical Biology was the first organized effort in the industry to focus on Translational Physiology. Douglas later further developed this approach when he reorganized Research and Development in Hoechst Marion Roussel into Target identification and Lead Finding, Lead Optimization (the successor to Clinical Biology) and Product Realization groups. Lead Optimization covered animal pharmacology, animal and human safety (Phase 1), through Phase IIa - Proof of Concept studies in select disease subsets. Product Realization covered Phase IIb, Phase III, and Approval.

Progress in Drug Discovery

Development accelerated starting in the late 1980s as a target or mechanism-based approach involving enzymes and receptors synergized with the traditional physiological approach of screening compounds in small animal models, such as using the spontaneously hypertensive rat to evaluate compounds that controlled blood pressure or using the rat tail-flick test to assess compounds for pain reduction.(10, 11) The mechanism-based approach synergized with gains in high throughput screening and combinatorial chemistry. However, what was lagging was structural biology. Progress in X-ray crystallography and other methods to rapidly display the structure of enzymes and particularly membrane bound receptors trailed the other advances. The announcement of the deciphering of the human genome was the potential breakthrough that was needed. It stimulated application of newly developing ideas around using chemical biology for drug discovery.

Step three was the development of the chemical biology platforms. Chemical biology was introduced in 2000 to take advantage of genomics information, combinatorial chemistry, improvements in structural biology, high throughput screening, and various cellular assays that the physiologists used and could now genetically manipulate to find and validate targets and leads. These assays included high content multiparametric analysis of cellular events using automated microscopy and image analysis to quantify cell viability, apoptosis, cell cycle analysis, protein translocation, and phenotypic profiling.(12–14) Reporter gene assays were used to assess signal activation in response to ligand-receptor engagement, and ion channel activity using voltage-sensitive dyes or patch-clamp techniques were used to screen neurological and cardiovascular drug targets.(15)

In 2000, the pharmaceutical industry was working on about 500 targets, including those for G-protein coupled receptors (45%), enzymes (25%), ion channels (15%), and nuclear receptors (~2%).(16) During his keynote speech at the Drug Discovery Conference, Douglas introduced the concept of chemical biology platforms in industry, which he saw as the way to benefit from the incredible new information that was provided by genomics data.(17) While it is not routine in industry for platforms developed to be published, this platform was described in the Journal of Business Chemistry.(1) The Chemical Biology Platform was an approach that incorporated organizational dynamics with a focus on targets that were mechanistically linked to the disease. Teams using these multidisciplinary platforms would be able to use genomic data to predict protein structures and interactions, which would facilitate identification and validation of drug targets, optimization of drug candidates, and a deeper understanding of biological processes, as well as potentially uncover mechanisms of side effects and identify susceptible patients.(18)

The development of this experimental pipeline actually expanded the clinical biology concept that led to the lead optimization group within traditional Research and Development. In effect, Clinical Biology became a Translational Physiology team. Here, we describe the chemical biology platform and highlight how its use has driven the development of novel compounds for clinical use (Figure 2) The foundation of the chemical biology platforms was built on the power that genomics and related tools would provide for identifying and validating drug targets, optimizing drug candidates, improving safety and efficacy of biopharmaceuticals, and understanding biological processes. The platform benefited from the knowledge gained from the interaction of compounds and from combinatorially produced libraries of small molecules, with protein targets of interest. The platform was a series of strategic steps to determine whether a newly developed compound could translate into clinical benefit. Having a target compound is fundamental to the process. Such an approach has been applied to the development of a novel cancer immunotherapeutic platform using tumor-targeting mesenchymal stem cells coupled with a protein vaccine.(2)

Figure 2.

Flowchart providing detailed overview of the stages involved in bringing a new drug from lab to market. The comprehensive process of drug development spans from initial research to post-market surveillance. The process is divided into two main phases: Research and Development. The Research Phase includes the initial screening of chemical compounds, culturing proteins for further study, identifying and validating biological targets, discovering potential lead compounds, and applying chemical techniques to biological systems.

The Development Phase includes studying the effects and safety of compounds, conducting trials in phases (Phase I, IIa, IIb, III) to test efficacy and safety in humans, optimizing leads and realizing products, gaining regulatory approval, and monitoring post-market. The ultimate goal of this process is to use Translational Physiology to develop means for (originally termed Personalized) Precision Medicine. --- refers to a structural collaboration. Created in BioRender, https://BioRender.com/w62g980.

While chemical biology uses chemical techniques and small molecules to elucidate the structure and function of biological elements, the chemical biology platform goes further. It represents a multidisciplinary approach that integrates chemical and biological (especially genomic) insights. This platform identifies the best biomarkers that characterize disease subsets and optimize discovery and development of treatments for patients with the specific disease. It also enables the use of small molecules to probe the mechanisms responsible for both aspects of efficacy and side effects. The impact of the chemical biology platform approach can be characterized by the following 6 key elements (Table 1):

1. Identification of Chemical Parameters: The first step involves selecting a chemical parameter that characterizes the disease state. This parameter, which is instrumental in understanding the disease, would later evolve in terminology into what we recognize today as a biomarker. Biomarkers are not only chemical entities but can also be genetic or imaging markers that can serve as critical indicators of disease progression and therapeutic response, enabling more precise assessments in clinical trials. Thus, the first step is to establish a suitable output measurement.

2. Selection of Measurement Systems: Next, it is essential to identify cellular or tissue systems where the chosen parameter can be measured. This involves using in vitro models that reflect the biological mechanisms of the disease and provide a more nuanced understanding of how the compound interacts with these systems. Genomic tools can be used to better understand normal and disease biological processes.

3. Application in in vivo Animal Models: The development of appropriate in vivo animal assays—both normal and diseased—is a crucial next step. These assays provide insights into the pharmacodynamics and pharmacokinetics of compounds, evaluate effects in a living organism that collectively encapsulate all biological processes before transitioning to human trials. There are several types of gene-based animal disease models, including transgenic models, knock-out and knock-in models, CRISPR/Cas9 gene-edited models, and humanized models, to name a few types. This step underscores the critical importance of physiology in this process and throughout the process.

4. Application in Human Disease Models: Selecting a human disease model or therapeutic indication in which the parameter can be measured is a critical step. The model selected needs to allow for the assessment of compound efficacy in a clinical context, focusing on patient populations that could benefit from the treatment. Genetic markers are increasingly important in identifying patients and appropriate treatments. Examples include: BRCA1 and BRCA 2 in breast cancer patients and HLA-B27 in patients with Ankylosing Spondylitis.(19–22)

5. Demonstration of Efficacy: A core objective is to demonstrate that the compound induces the desired changes across all selected systems or models without producing unacceptable changes in other systems. This includes verifying that the biochemical changes observed in preclinical studies are replicated in human disease models. Of note, many times the original disease target does not end up being the final disease target. Originally, indications that could make the most profit were targeted. However, this evolved into selecting indications that could most quickly demonstrate efficacy and safety. This sped up the process to approval and increased confidence for making go/ no go decisions. Once approved, drugs could be tested for new additional indications because approved drugs can be prescribed for off label use and approval in one indication makes it easier to recruit candidates to participate in clinical trials for the other indications. There are a number of examples, including sildenafil (Viagra) that was approved for the narrow indication of pulmonary arterial hypertension and later indicated for erectile dysfunction; pembrozilzumab (keytruda) that was approved for narrow indication of melanoma and later indicated for non-small cell lung cancer, head and neck cancer, and bladder cancer; and finasteride (proscar) that was approved for narrow indication of benign prostatic hyperplasia and later indicated for male pattern baldness.(23–25)

6. Linking Changes to Clinical Benefit: Finally, the most significant challenge is to establish that the changes in the biomarker in the human disease model directly correlate with measurable clinical benefits. This step is critical for justifying further development and investment in the compound. Through this systematic approach, a framework is created that not only assesses the biological activity of compounds but also provides a clearer pathway to correlate biological activity with clinical efficacy. The measurement of BCR-ABL expression, which is a hallmark of Chronic Myeloid Leukemia, is used to evaluate the response or resistance to Gleevec and is a well-known example of the importance of genetic biomarkers.(26)

Table 1.

The Six Key Elements of the Chemical Biology Platform

1. Identification of chemical parameters (biomarkers) that signify disease state

2. Selection of measurement systems that reflect cell and tissue physiology

3. Animal assays to demonstrate in vivo efficacy and safety

4. Human disease models that have biomarker(s) that can be measured

5. Demonstration of desired change across all selected models

6. Linking Changes to Clinical Benefit

The Evolution to Precision Medicine

Biomarkers and concepts of precision medicine emerged from clinical biology and the chemical biology platform as logical extensions of the original ideas. The concept of biomarkers became increasingly important as our understanding of diseases and therapeutic responses deepened. By identifying specific biological markers associated with disease states, researchers could develop more targeted therapies catered to individual patient needs.

In a similar theme, Koch’s postulates were first developed in the 19th century as a way to establish microorganism function and were modified in the 20th century to include methods to establish molecular causality.(27) This shift in reliance on biomarkers to establish disease or test effect of treatment laid the groundwork for the development of precision medicine. Precision medicine represents a paradigm shift in healthcare, focusing on tailoring treatments based on individual characteristics, including genetic, environmental, and lifestyle factors. The realization that not all patients respond similarly to the same treatment has driven the need for more refined approaches to drug development and patient care.

The Chemical Biology Platforms integrated approaches from various scientific disciplines, including chemistry, biology, molecular biology, and informatics, to enhance the efficiency of drug discovery and development. The platform can also be coupled with other strategies to improve experimental design, including using an investigative journalism approach to design mechanistic experiments that answer the what, where, when, how, and why questions to bridge observations to mechanisms.(28) This is becoming more apparent as we learn more about the contributions of epigenetic mechanisms. This platform can also be incorporated into training programs, such as basic science and clinical research summer programs, courses on strategies to advance clinical trial design, and the recently developed open online course on cell therapy manufacturing workforce that includes design, implementation, and evaluation considerations.(29–33)

Contributions of Chemical Biology Platforms

The Chemical Biology Platforms facilitated several key advancements (Table 2):

1. More Effective Collaborative Research: Although, organizationally, responsibility for running the Chemical Biology Platforms was placed under the Head of Target Validation and Lead Generation, members of the Chemical Biology Platforms came from all major disciplines across Research, Development, and Post-Marketing Surveillance and Knowledge Management. Information was used from all disciplines, and genomics-based tools were used to find best targets and treatments for specific patients. By promoting interdisciplinary collaboration, these platforms enabled researchers to share knowledge and resources more effectively. This collaborative approach accelerated the discovery process, leading to faster identification of promising drug candidates.

2. Enhanced Drug Screening: The integration of advanced technologies, such as high-throughput screening and in silico computational modeling, allowed for more efficient evaluation of compounds. A key component of enhanced drug screening is the integration of molecular modeling, which allows researchers to simulate and visualize the interactions between drug candidates and biological targets at the atomic level. This approach provides critical insights into binding affinities, conformational changes, and potential off-target effects, thereby refining the selection of lead compounds with optimal target-effector interaction properties. Importantly, these strategies are part of an iterative process—once primary target compounds are identified, molecular modeling and other screening techniques are repeatedly applied to optimize drug efficacy, reduce toxicity, and improve pharmacokinetic profiles. This cycle of refinement is essential for developing more effective and safer therapeutics.

   This improvement in drug-screening is now being further enhanced by AI-powered molecular modeling, which allows researchers to rapidly simulate, visualize, and predict interactions between drug candidates and biological targets at the atomic level. This AI-driven approach significantly accelerates and refines lead compound selection by providing critical insights into binding affinities, conformational changes, and highly accurate predictions of potential off-target effects and ADMET properties, thereby reducing late-stage attrition.

   Researchers could quickly assess large libraries of potential drugs against specific biological targets, identifying candidates with the best likelihood of success.

3. Improved ability to understand complex biological structures: By coupling the platform with advances in microscopy, x-ray crystallography, NMR spectroscopy, and computational modeling, researchers could rapidly test large numbers of compounds for ability to bind to specific receptors, aiding in drug discovery efforts.(34)

4. Optimized Clinical Trial Design: The use of biomarkers and human disease models within Chemical Biology Platforms improved both design and execution of clinical trials. By targeting specific patient populations based on biomarker profiles, researchers could enhance probability of demonstrating clinical benefit, ultimately leading to higher success rates in drug approval. Identifying patients most likely to benefit is an iterative process that can be aided by harnessing in silico approaches to systematically improve drug efficacy.

5. Economic Implications: The strategic and economic implications of combining drugs with clinical biomarkers cannot be overstated. By focusing on patient-specific therapies, pharmaceutical companies could not only improve patient outcomes but also reduce the financial risks associated with drug development. Fewer failed large-scale trials would result in a more efficient use of resources, thereby maximizing the return on investment.

Table 2.

Advantages of Using a Chemical Biology Platform

• More effective interdisciplinary collaborations

• Enhanced drug screening capabilities

• Improved ability to understand complex biological structures

• Better clinical trials (design & execution)

• Enhanced economic implications

Challenges and Future Directions: the next steps

While the advancements in chemical biology, translational physiology, and precision medicine have been significant, challenges remain. The major scientific challenge is to find solutions for the tough problems, which require holistic, multi-disciplinary approaches from teams of scientists that come from different backgrounds and who can reciprocally enrich thinking for all team members.(35) This historical review provides a roadmap for systematically addressing the remaining tough clinical problems. The complexity of human physiology and the heterogeneity of diseases necessitate ongoing research to identify and validate reliable biomarkers. Including representation from all gender, racial, and ethnic groups increases the selectivity and specificity of assessment. Additionally, regulatory frameworks must adapt to accommodate the innovative approaches being employed in drug development and the reality of the big data challenge.

The emergence of digital technologies and data analytics presents new opportunities for enhancing precision medicine. While many consider big data approaches to be primarily observational and hypothesis-generating, coupling -omics technologies with the six-step approach of the chemical biology platform will generate studies that drive our understanding of biological processes, including drug interactions. Coupling big data technologies with integration of artificial intelligence and machine learning will facilitate and accelerate the identification of novel biomarkers, the design of novel drug molecules, improvement of patient stratification, and enhance repurposing of known drugs, thus further refining treatment approaches. For example, using these approaches to catalog patient populations by most to least affected by a drug will help to tailor clinical trials such that instead of testing in a general population, the trial can be specific for those most likely to benefit.(36) Common mathematical modeling frameworks can be used to describe cell behavior, dynamics, and interactions of proliferation as applicable to cancer.(37) A big data approach will also address biomarker reproducibility challenges and cost-effectiveness concerns of precision medicine trials by tailoring who is enrolled. The cost is also a concern because often a companion diagnostic needs to be developed and used to identify the ideal patient for the new drug. This may also help federal regulatory hurdles by improving both time of testing as well as specificity of patient group targeted. By focusing on the relationship between biological parameters and clinical outcomes, we have paved the way for precision medicine, offering the promise of more effective and tailored therapies for patients.(38)

The promise of the mechanism-based approach in drug discovery still faces significant hurdles. Perhaps the biggest challenge at the forefront is that many diseases do not have a simple, well-understood cause, or their etiology remains entirely unknown. In addition, living organisms have compensatory systems; if you block a key target, the body might just find another way around it, meaning you still will not get the desired therapeutic effect despite being entirely correct in dissecting the mechanistic pathway(s) involved. Another major hurdle is that many biological targets are structurally complex. Pinpointing their active site can be incredibly difficult, and when we cannot find a way for a drug to interact, we often label these targets as undruggable.

As we continue to navigate the complexities of human health, the lessons learned from this historical review can guide future innovations in both the academic arena and the pharmaceutical industry by providing a foundation of knowledge that helps us understand what has worked, what has not, and why in both cases. These insights are invaluable for shaping future research directions, avoiding past mistakes, and building on successful strategies. The evolution of drug discovery has ranged from serendipitous findings to rational design and along the way we have learned the importance of integrating disciplines such as molecular biology, computational modeling, and systems pharmacology into the foundation of translational physiology.

Both academia and the pharmaceutical industry benefit from lessons learned by refining methodologies, improving regulatory frameworks, and fostering innovation. The process of scientific advancement is inherently iterative, meaning that each new discovery builds on and is informed by prior knowledge. This cyclical learning model ensures that future innovations are not only more effective but also more efficient and ethically grounded. In conclusion, learning the history of the chemical biology platform can provide a means for the next generation of physiology researchers to more quickly learn how to design experiments. Because this platform centers on concepts in physiology, understanding the integrative capabilities of the platform will better prepare students from all disciplines to engage in translational research.