In silico clinical trials

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An in silico clinical trial is an individualised computer simulation used in the development or regulatory evaluation of a medicinal product, device, or intervention. While completely simulated clinical trials are not feasible with current technology and understanding of biology, its development would be expected to have major benefits over current in vivo clinical trials, and research on it is being pursued.


The term in silico indicates any use of computers in clinical trials, even if limited to management of clinical information in a database.[1]


The traditional model for the development of medical treatments and devices begins with pre-clinical development. In laboratories, test-tube and other in vitro experiments establish the plausibility for the efficacy of the treatment. Then in vivo animal models, with different species, provide guidance on the efficacy and safety of the product for humans. With success in both in vitro and in vivo studies, scientist can propose that clinical trials test whether the product be made available for humans. Clinical trials are often divided into four phases. Phase 3 involves testing a large number of people.[2] When a medication fails at this stage, the financial losses can be catastrophic.[3]

Predicting low-frequency side effects has been difficult, because such side effects need not become apparent until the treatment is adopted by many patients. The appearance of severe side-effects in phase three often causes development to stop, for ethical and economic reasons.[2][4][5] Also, in recent years many candidate drugs failed in phase 3 trials because of lack of efficacy rather than for safety reasons.[2][3] One reason for failure is that traditional trials aim to establish efficacy and safety for most subjects, rather than for individual subjects, and so efficacy is determined by a statistic of central tendency for the trial. Traditional trials do not adapt the treatment to the covariates of subjects:

  • Taking account of factors such as the patient's particular physiology, the individual manifestation of the disease being treated, their lifestyle, and the presence of co-morbidities.[4][6]
  • Compliance, or lack thereof, in taking the drug at the times and dose prescribed. In the case of a surgically implanted device, to account for the variability in surgeons’ experience and technique, as well as the particular anatomy of the patient.[7] However, adjusting the evaluation of the study for noncompliance has proved difficult. Such adjustments often bias the results of the study, and so many health authorities mandate that clinical trials analyse the data according to the intention to treat principle.


Accurate computer models of a treatment and its deployment, as well as patient characteristics, are necessary precursors for the development of in silico clinical trials.[5][6][8][9] In such a scenario, ‘virtual’ patients would be given a ‘virtual’ treatment, enabling observation through a computer simulation of how the candidate biomedical product performs and whether it produces the intended effect, without inducing adverse effects. Such in silico clinical trials could help to reduce, refine, and partially replace real clinical trials by:

  • Reducing the size and the duration of clinical trials through better design,[6][8] for example, by identifying characteristics to determine which patients might be at greater risk of complications or providing earlier confirmation that the product[5] or process[10] is working as expected.
  • Refining clinical trials through clearer, more detailed information on potential outcomes and greater explanatory power in interpreting any adverse effects that might emerge, as well as better understanding of how the tested product interacts with the individual patient anatomy and predicting long-term or rare effects that clinical trials are unlikely to reveal.[9]
  • Partially replacing clinical trials in those situations where it is not an absolute regulatory necessity, but only a legal requirement. There are already examples where regulators have accepted the replacement of animal models with in silico models under appropriate conditions.[11] While real clinical trials will remain essential in most cases, there are specific situations where a reliable predictive model can conceivably replace a routine clinical assessment.

In addition, real clinical trials may indicate that a product is unsafe or ineffective, but rarely indicate why or suggest how it might be improved. As such, a product that fails during clinical trials may simply be abandoned, even if a small modification would solve the problem. This stifles innovation, decreasing the number of truly original biomedical products presented to the market every year, and at the same time increasing the cost of development.[12] Analysis through in silico clinical trials is expected to provide a better understanding of the mechanism that caused the product to fail in testing,[8][13] and may be able to provide information that could be used to refine the product to such a degree that it could successfully complete clinical trials.

In silico clinical trials would also provide significant benefits over current pre-clinical practices. Unlike animal models, the virtual human models can be re-used indefinitely, providing significant cost savings. Compared to trials in animals or a small sample of humans, in silico trials might more effectively predict the behaviour of the drug or device in large-scale trials, identifying side effects that were previously difficult or impossible to detect, helping to prevent unsuitable candidates from progressing to the costly phase 3 trials.[12]

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 This article incorporates text available under the CC BY 4.0 license.

  1. ^ This sense of the term was used in 2011 in a position paper from the VPH Institute commenting on the green paper written ahead of the launch of the European Commission Horizon 2020 framework programme. VPH greenpaper
  2. ^ a b c Arrowsmith, John; Miller, Philip (1 August 2013). "Trial Watch: Phase II and Phase III attrition rates 2011–2012". Nature Reviews Drug Discovery. 12 (8): 569. doi:10.1038/nrd4090. PMID 23903212.
  3. ^ a b Milligan, P A; Brown, M J; Marchant, B; Martin, S W; van der Graaf, P H; Benson, N; Nucci, G; Nichols, D J; Boyd, R A; Mandema, J W; Krishnaswami, S; Zwillich, S; Gruben, D; Anziano, R J; Stock, T C; Lalonde, R L (14 March 2013). "Model-Based Drug Development: A Rational Approach to Efficiently Accelerate Drug Development". Clinical Pharmacology & Therapeutics. 93 (6): 502–514. doi:10.1038/clpt.2013.54. PMID 23588322.
  4. ^ a b Harnisch, L; Shepard, T; Pons, G; Della Pasqua, O (February 2013). "Modeling and Simulation as a Tool to Bridge Efficacy and Safety Data in Special Populations". CPT: Pharmacometrics & Systems Pharmacology. 2 (2): e28. doi:10.1038/psp.2013.6. PMC 3600759. PMID 23835939.
  5. ^ a b c Davies, M. R.; Mistry, H. B.; Hussein, L.; Pollard, C. E.; Valentin, J.- P.; Swinton, J.; Abi-Gerges, N. (23 December 2011). "An in silico canine cardiac midmyocardial action potential duration model as a tool for early drug safety assessment". AJP: Heart and Circulatory Physiology. 302 (7): H1466–H1480. doi:10.1152/ajpheart.00808.2011. PMID 22198175.
  6. ^ a b c Hunter, P.; Chapman, T.; Coveney, P. V.; de Bono, B.; Diaz, V.; Fenner, J.; Frangi, A. F.; Harris, P.; Hose, R.; Kohl, P.; Lawford, P.; McCormack, K.; Mendes, M.; Omholt, S.; Quarteroni, A.; Shublaq, N.; Skar, J.; Stroetmann, K.; Tegner, J.; Thomas, S. R.; Tollis, I.; Tsamardinos, I.; van Beek, J. H. G. M.; Viceconti, M. (21 February 2013). "A vision and strategy for the virtual physiological human: 2012 update". Interface Focus. 3 (2): 20130004. doi:10.1098/rsfs.2013.0004. PMC 3638492. PMID 24427536.
  7. ^ Viceconti, Marco; Affatato, Saverio; Baleani, Massimiliano; Bordini, Barbara; Cristofolini, Luca; Taddei, Fulvia (2009). "Pre-clinical validation of joint prostheses: a systematic approach". Journal of the Mechanical Behavior of Biomedical Materials 2: 120–127.
  8. ^ a b c Erdman, A. G.; Keefe, D. F.; Schiestl, R. (March 2013). "Grand Challenge: Applying Regulatory Science and Big Data to Improve Medical Device Innovation". IEEE Transactions on Biomedical Engineering. 60 (3): 700–706. doi:10.1109/TBME.2013.2244600. PMID 23380845.
  9. ^ a b Clermont, Gilles; Bartels, John; Kumar, Rukmini; Constantine, Greg; Vodovotz, Yoram; Chow, Carson (October 2004). "In silico design of clinical trials: A method coming of age". Critical Care Medicine. 32 (10): 2061–2070. doi:10.1097/01.CCM.0000142394.28791.C3.
  10. ^ Agarwal, Yash (2019-02-15). "New Technological Breakthroughs for Patient-Specific Healthcare and Schizophrenia". Retrieved 2019-04-01.
  11. ^ Kovatchev, BP; Breton, M; Man, CD; Cobelli, C (January 2009). "In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes". Journal of Diabetes Science and Technology. 3 (1): 44–55. doi:10.1177/193229680900300106. PMC 2681269. PMID 19444330.
  12. ^ a b Viceconti, Marco; Morley-Fletcher, Edwin; Henney, Adriano; Contin, Martina; El-Arifi, Karen; McGregor, Callum; Karlstrom, Anders; Wilkinson, Emma. "In Silico Clinical Trials: How Computer Simulation Will Transform The Biomedical Industry An international research and development roadmap for an industry-driven initiative" (PDF). Avicenna-ISCT. Avicenna Project. Retrieved 1 June 2015.
  13. ^ Manolis, E; Rohou, S; Hemmings, R; Salmonson, T; Karlsson, M; Milligan, P A (February 2013). "The Role of Modeling and Simulation in Development and Registration of Medicinal Products: Output From the EFPIA/EMA Modeling and Simulation Workshop". CPT: Pharmacometrics & Systems Pharmacology. 2 (2): e31. doi:10.1038/psp.2013.7. PMC 3600760. PMID 23835942.

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