Translational Medicine Paradigms in Clinical Trials


The drug development landscape is markedly different today than it was a decade age. Basic science discoveries have piled up, waiting for a translational framework to get them tested fast over the clinical development cascade for reaching the physician’s desk as soon as possible. The age of molecular therapeutics has emerged facilitating rationally designed early proof of concept studies for a quick go no go decision. Translational medicine methodology in early clinical trials (Phase I and II) is an important aspect of development of new generation of therapeutics as it is necessary to implement radically different early phase clinical trial designs and to validate novel biomarkers for the full potential of new molecular entities to be realized.

Benefits of translational medicine tools are experienced in both early phase as well as late phase clinical development. In early clinical phases it provides feedback to pre-clinical scientists for further evaluations if needed. In the final pre-approval clinical trials it provides an insight into the target populations which may have a selectively better outcome, opening up the doors for a personalized medicine paradigm. Appropriate biomarkers utilized in clinical evaluations can stratify patient populations or provide a quantitative evidence of drug benefit.

Efforts made together by scientists, researchers, and physicians make translational research a truly interdisciplinary science for improving healthcare through innovations. Apart from being a fast emerging clinical trials market, India is fast moving towards being a valuable contributor towards drug discovery by adopting a translational framework at par with the world’s best.

Translational Research in Clinical Trials

The clinical research landscape has had a paradigm shift and is viewed differently than was a decade ago. There is a marked advancement in basic sciences especially the biotechnology advancements in molecular targeting, immunology and -omic sciences creating numerous opportunities and new therapeutic concepts for drug discovery.

As an example, cancer research has received much impetus and is on the priority list of translational biologists, regulatory bodies and funding agencies. A paradigm shift has been seen in cancer drug development wherein previously cytotoxic molecules used to be screened in the laboratory for further testing in early clinical trials with toxicity as main endpoints. Nowadays a shift towards mechanism based rational drug discovery has heralded, with check points at
the levels of proof of mechanism (POM) and proof of concept (POC) studies entering an era of molecular therapeutics.

A quick evaluation of new molecular entities is at the center stage of translational research strategies. Early clinical trials (Phase I and II) with the utilization of novel biomarkers for early proof of concept with newer study designs facilitate a quick go-no-go decision in today’s attrition ridden pharmaceutical pipeline. Retrograde feedback to pre-clinical research for therapeutic optimizations can be strategized by the use of “proof of principle with mechanistic analysis”. The late phases of type I translational research (phase III clinical trials) is increasingly being defined by the pharmacogenomic differences, driving drug development towards a personalized medicine paradigm.

Clinically important biomarkers are needed to facilitate regulatory and therapeutic decision making regarding potential drug candidates and their indications in order to help bring new therapeutics to the right patient more efficiently than they are today.


Figure 1: Biomarker categories: target, mechanism and clinical (Frank and Hargreaves 2003)
Biomarkers can be categorized into three separate categories on the basis of their contribution to the logic of a clinical development plan. Although they seem to coincide with the three phases of drug development, the objective is to use them as soon as possible, first to confirm hitting the target and then to test two concepts, namely, that hitting this target affects the pathophysiological mechanism and altering this mechanism affects clinical outcome

Translational Research Complexities

Realization of the benefits of translational research as a major driver to drug discovery and development imposes new ethicolegal, logistical, and operational constraints. It also demands an availability of sophisticated technology, imaging modalities, advanced laboratories, infrastructural up-gradations and continued trainings and commitment (Lehmann et al 2003).
Clinical trials designed on translational ideology generate data with biomarker based endpoints and rely on a large extent on the quality and validity of the laboratory assays and analyses.

An answer provided to the complexity in Translational Research is ‘Semantic Data Integration’. The biggest challenge in today’s cross-domain research endeavors is still the integration of data from many heterogeneous resources into coherent, contextualized information with their interrelationships. More flexible and extensible solutions are needed to fulfill the demands of translational research. Semantic integration methods provide coherence, harmonize synonyms
and different terminologies, and provide an extensible data integration platform and interactive knowledge base for relevant network analysis. The possibility of usefully integrate and traverse systems-oriented networks rapidly and easily, without losing the underlying complexity, is important. Establishing short, actionable inferences about targets, drug interactions, disease states and treatments, using combined clinical, -OMICs and molecular phenotypic data in conjunction with mechanistic insights from public knowledge networks presents a remarkable step ahead in translational medicine. The ability to more efficiently and effectively integrate and search data and public knowledge is a step towards generalized use in patient-centric personalized medicine (Gombocz 2011).

Validation of Translational Methods

The biomarker response to a new therapeutic should have sufficient data to support its reproducibility and validity. The molecular signatures or assays utilized should be validated by using appropriate controls and with the use of standardized methodology. Quality control measures in translational strategies are important to the extent that development of tools for translational research may sometimes take as much time as drug development itself. This time may be well justified with the wider application of such tools over the drug development spectrum crossing across therapeutic areas or indications.

So far it has not been seen that increasing the number of molecules entering into clinical development also leads to an increase in late-phase trials or drug approvals. The number of highly promising drug candidates in Phase 3 clinical trials is more or less unchanged since 1997 (Turner, 2011). The bottleneck is mostly at the level of Phase 2 clinical trials. The alarmingly high failure rate at Phase 2 is a direct result of having a disproportionately large number of candidate drug targets and an insufficient capacity for a full validation of the properties and therapeutic potential of individual targets. Attrition in Phase 2 is mostly a result of an inappropriate strategy of taking as many candidates forward as possible with minimum upfront investment in clinical validation.

Latest translational research approaches highlight the handicap of the traditional linear process, which was established to strengthen existing hypotheses rather than to create insight into clinically important interactions among drugs, targets, pathways, and diseases. Whatever is learned in the clinical setting can – and must – influence our research in the laboratory. The pathway from bench to bedside and vice versa is gaining importance.

For a preliminary evaluation of pharmacological activity, efficacy, and safety of new molecular entities, translational research strategies utilize innovative, short-term clinical trials using small groups of human subjects. A useful translation ensures at the bench side by the use of data from such pilot clinical trials helping an early decision making in the development cascade and also guides the bedside research towards appropriate endpoints. Such small scale clinical trials also identify early on in the drug development process, the populations which will benefit the most, from a specific therapeutic entity. It further provides feedback to stratify the late phase clinical trials tending towards a personalized drug development paradigm and better chances of success.

Figure 2: A quick win, fast fail drug development paradigm (Paul et al, 2010).
a) The traditional paradigm of drug development; b) An alternative development paradigm referred to as quick win, fast fail. In this alternative pathway, technical uncertainty is intentionally decreased before the very expensive late stage clinical trials (Phase II and Phase III) by the demonstration of proof-of-concept (POC). This results in a decreased number of new molecular entities (NMEs) advancing into Phase II and III, but those that do advance have a higher probability of success (p(TS)) and launch. The savings gained from expensive investment in late-phase R&D failures are re-invested in R&D to further enhance R&D productivity. CS, candidate selection; FED, first efficacy dose; FHD, first human dose; PD, product decision.

Interdisciplinary Approach

Interdisciplinary clinical research endeavors bring together specialists from various disciplines like clinicians, pharmacologists, molecular biologists, biochemists, functional imaging specialists, analytical chemists, pathologists, statisticians, bioinformaticians, project management professionals, etc. to facilitate the translation of scientific discoveries into clinical outcomes.
This need for a network of specialties makes it important for developing comprehensive clinical development programs for efficient new drug discovery. Logistic support and efficient management are of utmost importance in such a translational research platform.

Translational Health Sciences in India

Recently the Translational Health Science and Technology Institute (THSTI) at Gurgaon, Haryana has been established as an emerging health biotech science consortium with an aim to create a revolutionary institutional environment for conduct of interdisciplinary translational research. The Indian Government has provided an initiative for adopting an innovative and globally focused agenda creating an opportunity for emerging as a global leader in generating and translating scientific and technological advancements into clinical practice. THSTI also has a partnership with the Harvard-MIT HST for overseeing the establishment of the institute and to provide necessary training to researchers.

India has already demonstrated to be a strategic location for global pharmaceutical companies for conducting quality clinical research. India provides an advantage in terms of a large patient pool, lower costs, highly trained physician investigators and English as the primary language of instruction for medical professionals. A wide network of investigative sites is available in India for conducting clinical trials in all therapeutic areas. This has been facilitated by incentives and regulatory support provided by the government. India also possesses a state of the art data- processing infrastructure for biostatistics and bioinformatics.

Clinical trials in India frequently undergo regulatory and sponsor audits. Indian investigators have established their superiority in procuring quality data, meeting International standards for submission to domestic and International regulatory approvals.

Adams and Branter (2010) have stated the current drug development cost to be more than $1 billion extending over a span of almost 15 years. Clinical trials take almost 30 to 50% of this development time and almost one-third of this is taken by patient enrollments. For most of the therapeutics the patent protected periods usually include the research and development time span and more and more drugs are soon falling down the patent cliff. Since Jan 2005, India is at par with developed nations by being compliant with the Trade Related Intellectual Property Rights Act (TRIPS). This scenario makes India a preferred place for global clinical trials keeping in view the demographic advantage. India has a large and heterogeneous population pool of over one billion with many regions considered as conserved gene pools for pharmacogenomic profiling of newer therapeutics. This is compounded by a fast accrual rate in clinical trials creating efficiency much needed by the pharmaceutical industry. India has adopted the international GCP and integrated it with the schedule Y as an essential component under the Drugs and Cosmetic Act 1945.

A Visiongain report, Pharma Clinical Trial Services: World Market 2011-2021 stated that the global revenues are expected to reach US$32.73 billion in 2015 and to exceed US$65 billion in 2021. This report also states that clinical trials in India and China will show a compound annual growth of more than 20%.

The Drug Controller General of India (DCGI) is responsible for giving regulatory approvals for the conducting of clinical trials in India. Approval is usually provided in 12 weeks from the date of submission. After the Investigational New Drug (IND) dossier is submitted to the DCGI, parallel submissions are done to the respective ethics committees of the potential clinical trial sites. The Institutional or Independent Ethics committees follow the ICH-GCP, and Schedule Y of the Drugs and Cosmetic Act of 1945. The ethics committees review research protocols in
almost 4 to 6 weeks. In India many institutions also have Scientific Review Committees (SRC) for reviewing the scientific rationale of the clinical trial for ensuring more safety and the well being of clinical trial patients. Once the clinical study is approved by the Scientific Review Committee the study is then submitted for approval to the ethics committee. DCGI approval is also mandatory for obtaining a Test License to import clinical trial supplies, which usually takes 2 weeks. Around 14 weeks in total are taken on an average to complete the above processes for initiating a clinical trial in India. After the regulatory approval is granted for a clinical trial in India, the Director General of Foreign Trade (DGFT) provides approvals for export of blood or other biological samples. This may take an additional 2-4 weeks.

In the wake of Research integration efforts by the Translational Health Science and Technology Institute of the Department of Biotechnology, Government of India and a healthy clinical trial infrastructure all round the country, India is all poised to bridge the gap between scientific discovery and clinical research as envisioned in the Critical Path Initiative of the FDA.


Frank R, Hargreaves R.,Clinical biomarkers in drug discovery and development, Nat Rev Drug Discov. 2003 Jul;2(7):566-80.

Gombocz E, Stanley R, Eshleman J, Rockey C, Semantic Data Integration:Answer to Complexity in Translational Research, IO Informatics, 2550 Ninth Street, Suite 114, Berkeley, CA 94710, US. (Accessed 30th October 2011)

Lehmann F, Lacombe D, Therasse P, Eggermont Alexander MM,Integration of Translational Research in the European Organization for Research and Treatment of Cancer Research (EORTC) Clinical Trial Cooperative Group Mechanisms, J Transl Med. 2003; 1: 2.

Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov. 2010 Mar;9(3):203-14. Epub 2010 Feb 19.

Turner M, Chapter 14, Embracing Change: A Pharmaceutical Industry Guide to the 21st Century; Littman BH, Krishna R, Translational Medicine and Drug Discovery, Cambridge University Press 2011

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