Towards virtual clinical trials?

Clinical trials are among the most critical and expensive steps in drug development. They are highly regulated by the various international health agencies, and for good reason: the molecule or new medical procedure being tested can potentially harm the patients. To date, randomized clinical trials are the most valuable to health authorities. However, even though studies are designed to generate as much data as possible while limiting bias and respecting patient safety, they are limited in terms of the parameters tested. For example, some molecules are designed to treat diseases affecting small numbers of patients, so it is very complicated and costly for clinical trial sponsors to recruit enough patients and the statistical power generated is sometimes too low to be interpreted with confidence.

Could a mathematical, computerized model of a patient totally replace, or at least supplement, the data generated by humans in a clinical trial?

This short article will try to develop the concept of in silico clinical trials through some notions and examples from the scientific literature. We hope that it can teach you more about this exciting field.

In silico clinical trials use virtual patients, i.e. mathematical models generated by an algorithm, mimicking our physiology and capable of reproducing, for example, the pharmacokinetics of a drug X 1 or their associated toxicity 2. They have many advantages, such as generating more confidence in the molecule being tested before any animal and/or human experiments. Increasing the statistical power of trials carried out on small populations; such as when a molecule is tested in orphan diseases. Eventually, this technology follows the 3 Rs rule of limiting the use of laboratory animals: Replace, Reduce, Refine.

Working with this concept, Sarrami-Foroushan et al 1 modelled the therapeutic effect of stenting in the treatment of intracranial aneurysms. The first step of the project was to check whether it was possible to replicate the data from existing studies and, secondly, to explore certain situations that would have required a more complicated set of patients to pool.

Based on “virtual” carotid anatomies (but modelled from real patients), the researchers were able to apply a set of models to reproduce the different physical mechanisms (fluid dynamics for the blood, for example) involved in the evolution of the aneurysm, and to observe the effect of the prosthesis on the diseased vessel (in this case, its occlusion). The aim was also to generate a model capable of comparing the effect of the prosthesis in a normotensive patient and in a hypertensive patient.

The predicted score was comparable to results already published in the literature, and allowed the exploration of new scenarios where, for example, the aneurysm has a more complex morphology and some patients are more difficult to recruit.

This example illustrates the strength that in silico technology will represent in the coming decades. Various health authorities, such as the FDA 2, are placing increasing emphasis on these predictions, as they reduce the cost and duration of clinical trials.

Another case study is that developed by Gutiérrez-Casares et al. 3 in the treatment of ADHD, using two different small molecules, lisdexamfetamine and methylphenidate.

The team first had to characterize the pathology and the drugs tested at the molecular level: in ADHD, the expression of certain proteins is altered, and the two molecules have a different mode of action. Sensitivity and efficacy may therefore be different in a patient depending on the molecule studied. The activity of these proteins was then correlated to clinical efficacy criteria. They generated a virtual population, demographically similar to the populations observed in the pathology, describing different protein profiles according to the “healthy” or “sick” status of the patient. Finally, the team used this virtual population to generate their pharmacokinetic profiles and simulate the concentration that the drug would have in their body.

Based on their protein profiles and by cross-referencing the generated efficacy data, the researchers were able to find the key proteins in the mechanism of action of both drugs. It is not only efficacy and safety data that can be generated via in silico testing, but also data fundamental to the drug’s mode of action that can be inferred.

To date, it is still complicated to adopt a holistic approach to the simulation of Human physiology. As the article by Gutiérrez-Casares et al. shows, the reliability of models is limited to what is already known.  The notion of digital twins is applicable to many fields, but may never be applicable to Health. However, with ever-increasing computing power and evolving clinical databases, models will come closer and closer to plausible outcomes. Where a phase 3 trial requires numerous patients, could in silico trials reduce this number and speed up the approval of new drugs on the market?

On the public side, initiatives such as the VPH Institute 5 and Avicenna Alliance 6 promote the use of in silico and contain multiple resources available to all to democratize the technology.

On the private side, there are companies such as InSilicoTrials 7, Novadiscovery 8 or the InClinico tool from the company InSilico 9 that offer platforms accessible to the various players in the health sector, and provide them with “ready-to-use” tools to initiate their own simulations.

Is it possible to imagine a future where these tools allow small and medium-sized biotechs to access phase 3 clinical trials without the financial means of a BigPharma? The ecosystem of the healthcare industries would then be more favourable to innovative and “risky” ideas, and not only to the historical players, capable of absorbing the heavy failure of a phase 3.

To go further:

1. Sarrami-Foroushani, A. et al. In-silico trial of intracranial flow diverters replicates and expands insights from conventional clinical trials. Nat. Commun. 12, 3861 (2021).
2. AltaThera Pharmaceuticals Announces FDA Approval for New Indications of Sotalol IV: A New and Faster Way to Initiate Sotalol Therapy for Atrial Fibrillation (AFib) Patients. 
3. Gutiérrez-Casares, J. R. et al. Methods to Develop an in silico Clinical Trial: Computational Head-to-Head Comparison of Lisdexamfetamine and Methylphenidate. Front. Psychiatry 12, 1902 (2021).
4. Marr, B. 7 Amazing Examples of Digital Twin Technology In Practice. Forbes 
5. VPH Institute | Virtual Physiological Human – International non-profit organisation. 
6. AVICENNA ALLIANCE. 
7. InSilicoTrials – Modeling and simulation in drug development. InSilicoTrials 
8. Novadiscovery. 
9. InClinico | Insilico Medicine.