Pains and Priorities of Disease and Drug Research: Trends in Target Identification, Characterization, and Validation


Recognition of the pathophysiologic contributors to disease states has become more specific than ever, fueling an increased focus on translational science and precision medicine within the pharma industry. Hence, identifying and developing novel drug targets and bringing new drugs to market can hold a great deal of promise for patients and their families. However, the process of innovation is associated with a number of pain points. Here, we will discuss some of these challenges and trends in disease and drug research in the context of drug target identification, characterization, and validation.

Pain points in target identification

While universal challenges exist in target identification, there are those that are specific to pharma labs vs. academic. We will discuss both types below.

Target identification is usually initiated via referencing published research or existing drug treatments, so it seems counterintuitive that pain points could arise at such a fundamental step in the process. However, in light of the wide range of concerns or considerations that influence the identification of a drug target, from reproducibility of prior studies to statistical power of existing investigations, this initial area of drug research could encounter challenges hindering the downstream process.

Some of the major pain points in target identification include:

  1. Collecting and analyzing target data

    Whether the target is novel or known, mining relevant information and data can become a major pain point.

    In the current era of precision medicine and gene therapy, researching genetic level data or studies are fundamental to identifying drug targets. Sometimes this information exists at the outset of research; other times, as in the case of rare diseases, the first step in developing a treatment involves first identifying the pathophysiology of the disease to eventually enable identification of the target. Since research varies based on the scope of the original study, existing publications may communicate fragmented data or case reports or small-scale studies devoid of the broader biology. Sifting through studies to determine which data are relevant or useful requires a lot of time and collaboration; the advent of big data has made this process even more time-consuming and technically complex.

    When a target is known, reflecting that there is documentation of previous relevance to the disease or drugs, the next step is the validation of the association of the target to the disease. If the research is not reproducible, which, according to a recent article in Nature 1, it is often the case, it may be challenging to confirm the fundamental correlation.

  2. Selection of technology

    Technological limitations can hinder research in a number of ways. For example, in the context of data management, clunky, outdated systems slow the pace of laboratory work and data analysis, and sometimes may not be able to adequately manage the large volumes of data emerging from high-throughput analysis or similar screening tools. Therefore, appropriate selection of technology and techniques, as well as software analysis and bioinformatics tools, is critical.

  3. Cost

    Pharma labs generally have greater access to funding than those in an academic setting. Academic labs, by virtue of their competitive grant funding, must run leaner than their industry counterparts, and follow the old adage of “publish or perish.” Subsequently, smaller academic labs are likely to have limited means to invest in technology or instruments with a high capital cost, thus negatively impacting their ability to expand their research capabilities.

  4. Infrastructure

    Academic settings may encounter specific challenges in the form of infrastructure. Many institutions are publicly funded, and therefore have greater oversight on spend and the bandwidth of research that might be conducted. This may translate to sharing of technology and/or resources between labs to achieve their research goals. In the context of target identification workflows, this might mean collaborating with other campus labs to utilize or leverage high end instruments.

Pain points in target characterization

After identifying molecular targets, the next step is characterization of the target(s) or biomarker(s) which could involve studying the molecular structure, conformation, binding affinity to other protein(s) or ligands, and metabolic or physiological impact further leading to validating its association with the disease process.

Common pain points encountered in this stage include:

  1. Technology

    Target characterization involves understanding the protein(s) at a molecular level (if protein is the target, which it is in most studies). As treatment approaches shift to precision medicine, labs trend increasingly toward next generation technologies to characterize the target(s) with greater accuracy and reliability. Furthermore, evolving technology advancements would require highly-trained scientists to use, interpret, and infer from such experiments involving characterization.

  2. Context of protein/target

    Protein/target functional interaction within the context of other proteins and/or cellular environment constitutes one of the most important steps in characterization. Phenotyping the target of interest via binding and functional assays leverages detection and imaging technologies resulting in huge amounts of data.

    Therefore, it is critical that the staff scientists are supported by informatics expertise in analysis and management of big data with the ability to translate it into biological context. Since there is a trend for greater physiologically relevant studies in vitro or ex vivo, there is a continued need for innovation around complex model systems such as 3D cell/tissue culture supported by enhanced detection and imaging technologies.

  3. Time availability

    In academia, principal investigators (PIs) usually have joint responsibilities including such as teaching, research mentorship, as well as administrative activities such as grant applications and departmental committees. As a consequence, PIs are faced with a narrower bandwidth for actual research. Academic environments that can provide supplemental administrative support and/or dedicated sabbatical time would ensure greater focus on research productivity.

  4. Collaboration

    Due to the lack of infrastructure, academic labs often collaborate to share resources necessary for the completion of research projects. These partnerships offer many other benefits such as complementing knowledge, however, the trade-off is management of multiple schedules, research teams, and joint output in terms of publications and possibly joint IP. This adds another layer of complexity and possible bottlenecks in the workflow or process.

  5. Reproducibility of data

    Inability to replicate results of existing research in both pharmaceutical and academic settings, while not uncommon, can undermine the validity of the contributing data which exerts pressure on the lab to justify their own results. This increased accountability also ultimately requires lab. bandwidth, and therefore can result in slowdowns.

Pain points in target validation

Target validation involves studying the association of a drug target with a disease phenotype. Determination and establishment of the confidence in this association largely represents the end point value of the drug that will be identified downstream as the lead candidate. For example, if the target association is weak or unable to establish multi-target association with the disease phenotype, it may reduce the value of target validation. On the contrary, if the validation is strong, the target is more likely to prove a valuable asset for further drug screening and development.

A few challenges encountered in target validation include:

  1. Availability of model systems

    Limited availability of relevant tissues, primary cells, or cell lines could potentially cause a fundamental bottleneck in the process of target validation. Without physiologic context, it is difficult to validate the association of target to disease. Emerging model systems such as siRNA or 3D cell cultures are not only appropriate alternate systems but also offer molecular and physiological relevance in vitro that potentially minimize cost and resources in animal models long term.

  2. Animal models

    Investments in animal housing including onsite veterinarians and supporting technical resources, as well as compliance with federal and institutional guidelines/regulations and ethical concerns can add to time and cost constraints on research labs.

    As a result, it is becoming increasingly important that labs have access to physiologically relevant model systems as early as target validation through preclinical drug development studies.

  3. Level of maturity in genomics

    The rapid pace of genomics discovery has made precision medicine a hotbed for pharma research. However, technology has not necessarily kept up with correlating genomics data to phenotypic profiles and the corresponding need to identify panel(s) of disease-specific targets and validate simultaneously. Often researchers either lack the technology to process and contextualize the vast amounts of genetic data available for a certain condition or cannot fully appreciate all of the genetic contributors in a multifactorial disease state. This can lead to bottlenecks and slow down research.

Trends in disease research

Genetic profiling of disease states, generally via biomarker identification, is of high interest to researchers. While the clinical symptoms, and the diagnostic/predictive markers of a disease may be well-established, the increasing accessibility to big data from genomics leads to further studies at the molecular pathophysiology level and effectiveness of existing treatments.

Additionally, recent progress in 3D cell culture and organoid technology offers key advantages. Organoid model systems not only replicate physiologic conditions, but also offers the ability to concurrently study the biophysical characteristics of a cell wall or the effect of changes in interstitial fluid pH on permeability which can enable a more mechanistic approach to drug research. Since organoid technology closely replicates human tissue, it may in the future offer an alternate platform to animal models. Hence, there is a strong potential for the 3D based tissue systems to become an industry standard.

Trends in drug research

Trends in drug research dovetail with disease research in their shared focus on precision medicine, but drug researchers must also factor in considerations such as assay-to-assay or batch-to-batch data reproducibility, cost, and productivity. Drug discovery and development is associated with financial ROI and other productivity metrics, particularly in light of the escalating costs associated with complex genetic endeavors and physiologically relevant studies using 3D cell cultures and organoid configurations. However, the trade-off would be an earlier attrition rate of drug candidates eventually conserving the total spend as well as delivering better and precision drugs to market.

Advancements in biomarker identification and characterization can also help researchers cross-study different disease states and drug targets. This can lead to marketable repurposing of drugs, which would benefit both drug companies and patients with novel forms of treatment.

Disease research and drug discovery have come a long way in recent years, with plenty of room for future growth. As pharma labs increase translational medicine activities, it will be important to consider the pain points discussed above, as well as new challenges and solutions that will likely emerge as technology continues to develop.