Indian Journal of Pharmacology Home 

[Download PDF]
Year : 2022  |  Volume : 54  |  Issue : 6  |  Page : 393--396

Translational research: Bridging the gap between preclinical and clinical research

Vidya Mahalmani1, Shweta Sinha2, Ajay Prakash3, Bikash Medhi3,  
1 Department of Pharmacology, J. N. Medical College, Belagavi, Karnataka, India
2 Department of Experimental Medicine and Biotechnology, PGIMER, Chandigarh, India
3 Department of Pharmacology, PGIMER, Chandigarh, India

Correspondence Address:
Bikash Medhi
Department of Pharmacology, PGIMER, Chandigarh

How to cite this article:
Mahalmani V, Sinha S, Prakash A, Medhi B. Translational research: Bridging the gap between preclinical and clinical research.Indian J Pharmacol 2022;54:393-396

How to cite this URL:
Mahalmani V, Sinha S, Prakash A, Medhi B. Translational research: Bridging the gap between preclinical and clinical research. Indian J Pharmacol [serial online] 2022 [cited 2023 Jun 2 ];54:393-396
Available from:

Full Text

Drug discovery and development are lengthy and expensive process that takes nearly 10–15 years with an average amount of more than $1–2 billion for the approval of every novel drug for clinical use.[1] Nonclinical studies are generally part of basic science research that generally evaluates the safety and tolerability of a drug. While choosing a preclinical model, it is very important to lay out a specific research question as well as to make certain that the selected model is suitable to serve the purpose. In spite of investing a huge amount in drug development, the rate of success of drugs reaching clinical trials is quite low. Nine out of ten drug candidates would fail in Phase I, II, and III clinical trials.[2],[3] The failure rates might be reduced by adopting strict criteria during the preclinical stages of drug development. Translational research referred to “bench-to-bedside” plays a vital role in interlacing the gap between basic science and clinical research. The major goal of translational research is to ensure that the majority of innovations progress to clinical trials and also have the maximum probability to be successful in terms of safety as well as beneficial to society. It is necessary for us to know about the in vivo/in vitro models being used, their drawbacks, applicability to various diseases, and translatability. Excluding at an early stage, the ones that are most likely to fail may reduce the overall expenditure of developing novel compounds.

Various factors should be taken into consideration during the conduct of preclinical studies. The animal species and strain chosen for a specific disease model should be chosen meticulously. Age, sex, and health of animals should mimic the clinical condition. For example, screening novel drug candidates in younger animals for conditions such as Alzheimer's disease (AD) and osteoarthritis would provide erroneous results as these conditions are diseases of the elderly age group and not mimic the clinical condition when the results are extrapolated from preclinical studies.[4] A single preclinical model cannot simulate or meet all the criteria of a clinical condition as validation of a model is an essential step in drug discovery and development. Therefore, a combination of animal models would serve the purpose better compared to a single model. Sample size of preclinical studies is quite small compared to clinical studies. There could be variation in the results when extrapolated because of the smaller size of the animal group. Hence, results cannot be generalized to clinical settings. Furthermore, most of the preclinical experiments are conducted under standard conditions that might probably not mimic a clinical scenario. Study designs in preclinical studies are robust and reproducible but does not hold good for clinical trials. In clinical trials, pertaining to various chronic diseases, quality of life serves as an important endpoint which is often evaluated by questionnaires. Unfortunately, this is not possible in the case of preclinical studies.

 Translational Relevance of Preclinical Models

Preclinical studies are an imperative part of clinical studies to assess an in vivo situation of biological pathways and physiology after interventions. However, most of these studies yield inconclusive data due to incorrect experimental design, animal model validation, duration of treatment, inadequate reporting, and subjective interventions.

Various pathophysiological mechanisms and potential targets have been explored in different preclinical models with respect to acute kidney injury (AKI), however, not confirmed in human studies. Till date, no AKI therapies have been proved to be efficacious. Therefore, it has been proposed to use validated animal models and AKI biomarkers for choosing samples in clinical trials. Larger sample size, kidney biopsies, and omics approach are vital steps to refine translational research in preclinical models.[5]

Genetically engineered mouse models are used for targeted therapies in cancer as they mimic the histology and biological behavior of human cancers. They help in validating newer anticancer drugs, identify tumor progression markers, evaluate therapeutic index, and explicate the contribution of epigenetic and environmental factors in tumorigenesis.[6] However, there are limitations of preclinical models in cancer research as well where preclinical models do not completely translate into clinical trials. A well-known example is the TGN1412 trial.[7] The drug TGN1412 is a humanized anti-CD28 monoclonal antibody developed for treating immunological disorders such as rheumatoid arthritis, multiple sclerosis, and cancers. TGN1412 tested on various animals including mice did not exhibit any toxic effects. However, the drug lead to catastrophic systemic organ failure in patients, although a subclinical dose was administered which was 500 times less than the animal dose found safe in preclinical studies.[8]

Another tragic mishap of BIA 10-2474, a FAAH inhibitor in Phase I clinical trial was a major setback to researchers. Out of 128 participants enrolled in the trial, 90 were administered with test compound while others were given the placebo. One of the participants who was subjected to multiple doses of test drugs was declared as brain dead while the other 5 suffered from irreversible brain damage. Magnetic resonance imaging of the affected individuals was suggestive of deep cerebral hemorrhage and necrosis. The possible causes for this mishap could probably be human error or off-target action of the investigational drug.[9]

 Challenges of Translational Research

Translational research requires a renewed outlook because of various hurdles such as limited resources, higher dropouts, and the long duration of time needed for novel drug development. Translational medicine is a process that necessitates repetitive interactive feedback between different disciplines for successful new drug development. Various government and private sectors, academic institutions, and pharmaceutical industries are involved. Lack of proper target, gap in knowledge about the disease pathophysiology, lack of safety, and enormous heterogeneity of sample population are various hurdles in translational medicine. The drug might exhibit effectiveness in only in a subset of the population as the target might exist only in that particular subset of patients. Despite the understanding of the pathophysiology of a disease, the mechanism of action of drug on different targets, the predictive utility of preclinical models is often low particularly studies pertaining to single-knockout disease models.[10],[11] Therefore, “the translational gap” that is the gap between bench and bedside research is also known as the “Valley of Death.” [Figure 1] depicts various challenges of translational research.{Figure 1}

 Suggestions to Improve Translational Research

To upgrade translational research, it is mandatory to refine ones' hypothesis prior experimenting them. By adopting this method, lot of time and resources would be saved, thereby increasing the probability of success. Integration of extensive data from in vitro/vivo and clinical studies will definitely aid in refining the objectives and target identification. This will help us to refine the translatability of findings of preclinical studies to clinical, thereby boosting the chances of successful drug development. In addition, an upcoming approach to refine translational research is by screening compound libraries and identification of candidate drugs rather than working on primary human cells or cell lines. Currently, three-dimensional organoids are adopted for the swift screening of drugs.[12],[13]

Another upcoming approach is the compound library screening strategy where they utilize so-called clinical trials in a dish (CTiD). With the help of CTiD techniques, one can test promising therapies for safety and efficacy on cells. Cells are procured from a particular sample population that allows to develop drugs for specific population.[14]

Furthermore, drug repurposing is other strategy to speed up the process of drug development and overcome the hurdles that arise in the early phases.[15],[16] With drug repurposing, drugs can be developed in a shorter span within 4–5 years with less risk of failure and at a lower cost, as such drugs will have already gone through the early stages of drug development. However, this holds good only if the dose of the drug and route of administration are alike. However, if a new indication demands a considerably larger dose, then the drug needs to undergo Phase I trials once more. At present, biomedical research funds the majority of short-term projects as they can be concluded in a short span of time. Nevertheless, such results obtained need to be validated with a larger cohort and longer study duration. The major obstacles for such studies are cost and the availability of sufficient resources. These can be overcome by collaboration between research organizations and pharmaceutical industries.

Machine learning is another approach by which predictions can be made as to how a novel compound would behave in discrete physical and chemical environments. Artificial intelligence (AI) has already gained popularity as it helps physicians in decision-making as well in the field of cancer diagnostics and immunotherapy.[17] However, there are limitations even with AI. Quality of data plays a major role in better predictions using AI. If the data are inaccurate, then the results will also be erroneous. There is still need for humans to put it all together. These technologies will only speed up drug development that would save our time and resources.

 Role of Bioresources in Translational Research

Bioresources such as human tissue play a vital role in the field of discovery of biomarkers and precision medicine. They help in identifying the targets and biomarkers that play a chief role in fruitful molecular-based therapies for specific disease subtypes. Few examples of how biospecimens improved medical therapies as well as increased cognizance of the biology of normal tissues and diseased ones' are quoted below. HER-2, a human epidermal growth factor receptor Type 2 (HER-2), is the target in gastric and breast carcinomas. Biospecimen has enabled us to know that overexpression of HER-2 in breast cancer is marked by increased recurrence rates and high mortality suggestive of poor prognosis.[18],[19]

Unpredictable differences between preclinical and human models might be answerable to the lack of safety and efficacy in human studies. Human tissues aid in evaluating safety as the toxicological studies performed using preclinical models will not simulate the human environment. They also help to replace the use of animals for preclinical studies and also find out potential adverse effects in vital organs that might be because of “off-targets” relevant to humans. However, the use of human tissues is not very common due to various logistic and technical glitches.[20]

In the case of organophosphorus and aluminum phosphide poisoning, the current treatment is not completely effective and data suggests that there is a lack of translational research in these fields. Therefore, there is an immense need for translational research for the treatment of pesticide poisoning as well.[21]

Melanoma is considered one of the most aggressive types of skin cancer associated with high mortality and resistance to currently used therapies. As drug discovery and development is a long and expensive process, computational approaches for anticancer therapy have been popular in the last few years.[22] Binimetinib and encorafenib are examples of in silico drug development approved by the Food and Drug Administration for the treatment of melanoma.[23] Better understanding of the pathogenesis of melanoma and the anticancer activity of a lead compound can be predicted well using computational approaches. Targeted therapy based on the discovery of driver mutations and implementation of checkpoint inhibitor-based immunotherapy constitutes a major breakthrough in the treatment of metastatic melanoma.[24] As of January 7th, 2023, 196 studies pertaining to the role of various biomarkers in melanoma have been completed.[25]

AD is one of the neurodegenerative disorders characterized by lack of complete knowledge of molecular mechanisms about the underlying pathology and lack of valid biomarkers and effective therapeutic options. Translational neuroscience enables in accelerating drug development by improving the design of clinical trials and outcomes. Furthermore, novel biomarkers can be discovered that will help in the early recognition of neurological disorders and in a better understanding of the underlying disease pathology. As of January 7, 2023, 255 studies pertaining to the role of various biomarkers in AD have been completed.[26]

Paxlovid which is the combination of nirmatrelvir and ritonavir has been shown to decrease the risk of hospitalization and death in COVID-19 patients by 88% if the drug combination is taken within 5 days of developing symptoms. However, it has been postulated to pose a probable threat of resistance to antivirals including treatments for HIV and hepatitis C by few researchers.[27]


Translational drug development strategy necessitates preclinical models to be meticulously chosen and performed under standard conditions. Various translational tools such as quantitative systems pharmacology, experimental clinical trials, and biomarkers can be adopted in support of the results obtained from preclinical studies.


1Hinkson IV, Madej B, Stahlberg EA. Accelerating therapeutics for opportunities in medicine: A paradigm shift in drug discovery. Front Pharmacol 2020;11:770.
2Dowden H, Munro J. Trends in clinical success rates and therapeutic focus. Nat Rev Drug Discov 2019;18:495-6.
3Takebe T, Imai R, 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 2018;11:597-606.
4Bouchlaka MN, Sckisel GD, Chen M, Mirsoian A, Zamora AE, Maverakis E, et al. Aging predisposes to acute inflammatory induced pathology after tumor immunotherapy. J Exp Med 2013;210:2223-37.
5Fiorentino M, Kellum JA. Improving translation from preclinical studies to clinical trials in acute kidney injury. Nephron 2018;140:81-5.
6Gutmann DH, Hunter-Schaedle K, Shannon KM. Harnessing preclinical mouse models to inform human clinical cancer trials. J Clin Invest 2006;116:847-52.
7Attarwala H. TGN1412: From discovery to disaster. J Young Pharm 2010;2:332-6.
8Suntharalingam G, Perry MR, Ward S, Brett SJ, Castello-Cortes A, Brunner MD, et al. Cytokine storm in a phase 1 trial of the anti-CD28 monoclonal antibody TGN1412. N Engl J Med 2006;355:1018-28.
9Scientists Speculate on What Caused the Bial Drug Testing Tragedy in France – Forbes. Available from: Last accessed on 10th December, 2022.
10Carini C, Seyhan AA, Fidock MD, van Gool AJ. Definitions and conceptual framework of biomarkers in precision medicine. In: Claudio Carini MF, van Gool A, editors. Handbook of Biomarkers and Precision Medicine. New York: Chapman and Hall/CRC; 2019. p. 2.
11Sabroe I, Dockrell DH, Vogel SN, Renshaw SA, Whyte MK, Dower SK. Identifying and hurdling obstacles to translational research. Nat Rev Immunol 2007;7:77-82.
12Chung K. Rapid drug screen using 3D tumor organoids. Sci Transl Med 2018;10:eaar7507.
13Sachs N, de Ligt J, Kopper O, Gogola E, Bounova G, Weeber F, et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 2018;172:373-86.e10.
14Fermini B, Coyne ST, Coyne KP. Clinical trials in a dish: A perspective on the coming revolution in drug development. SLAS Discov 2018;23:765-76.
15Scannell JW, Blanckley A, Boldon H, Warrington B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov 2012;11:191-200.
16Nosengo N. Can you teach old drugs new tricks? Nature 2016;534:314-6.
17Leiserson MD, Syrgkanis V, Gilson A, Dudik M, Gillett S, Chayes J, et al. A multifactorial model of T cell expansion and durable clinical benefit in response to a PD-L1 inhibitor. PLoS One 2018;13:e0208422.
18Burstein HJ. The distinctive nature of HER2-positive breast cancers. N Engl J Med 2005;353:1652-4.
19Mitri Z, Constantine T, O'Regan R. The HER2 receptor in breast cancer: Pathophysiology, clinical use, and new advances in therapy. Chemother Res Pract 2012;2012:743193.
20Available from: Last accessed on 10th December, 2022.
21Buckley NA, Roberts D, Eddleston M. Overcoming apathy in research on organophosphate poisoning. BMJ 2004;329:1231-3.
22Basith S, Cui M, Macalino SJ, Choi S. Expediting the design, discovery and development of anticancer drugs using computational approaches. Curr Med Chem 2017;24:4753-78.
23Cui W, Aouidate A, Wang S, Yu Q, Li Y, Yuan S. Discovering anti-cancer drugs via computational methods. Front Pharmacol 2020;11:733.
24Marra A, Ferrone CR, Fusciello C, Scognamiglio G, Ferrone S, Pepe S, et al. Translational research in cutaneous melanoma: New therapeutic perspectives. Anticancer Agents Med Chem 2018;18:166-81.
25Available from: Last accessed on 10th December, 2022.
26Available from: Last accessed on 10th December, 2022.
27Available from: Last accessed on 10th December, 2022.