In silico models – simulations of a complex system in the form of equations or rules – are becoming increasingly popular in the drug development process and across clinical research. Use of computational models and simulations offers significant advantages over human-based clinical trials in both operational factors and therapeutic outcomes.  

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Introduction of in silico

In silico computational models provide the tools to qualitatively and quantitatively evaluate various treatments on specific diseases and to test a larger set of different conditions (e.g. dosing). These models are abstract representations used to model human diseases, a concept which is often limited by in-vitro/vivo techniques. 

In comparison to in-vivo techniques, which are performed in whole organisms, in silico modelling offers more practical, economical experiments. Furthermore, computational methods limit the use of animal models in research which supports the rationale in designing novel, safe drug candidates.  

Clinical applications

Drug development

The process of drug development continues to experience issues in rising costs and achieving FDA approval. Innovative approaches are required to better identify drug targets and efficacy predictions. In silico methods like molecular docking are one type of solution to current problems in drug development.

Computer-aided drug design (CADD) is a group of methods which offer a cost-effective way of identifying drug candidates. There are two types of CADD approach: structure-based design (SB-CADD) and ligand-based design (LB-CADD). 

The LB-CADD method uses reference structures collected from the compounds known to interact with the target, and analyses their 2D/3D structure. The objective aim is to predict the mechanism and strength of binding for a specific molecule to the target. The benefit of using CADD methods is that they offer a higher probability of identifying compounds with the desired properties, increasing the chance of a compound overcoming the barriers of preclinical testing. 

One of the main limitations of pharmacophore-LBDD (pharmacophore, meaning the molecular unit responsible for specific biological interaction) is the complexity of molecular dynamics. This method is computationally-demanding and dependent on the size of the simulated systems, with analysis time ranging from hundreds of nanoseconds to microseconds. The issue with this is the limited time period, which is often too short to analyse protein folding – which can range from milliseconds to seconds. As a result, this can lead to “inadequate sampling” of protein conformations. 

Drug repurposing

In addition to optimising drug design, in silico models have been used in drug repurposing. An example of computational biology in drug development is network-based drug-repurposing (NB-DRP). In NB-DRP, the relationships between biological compounds are organised into networks in order to identify emerging properties at a network level. The network allows users to examine how cellular systems undergo different biological phenotypes under various conditions. 

In terms of structure, a network is created as a connected graph, in which each node represents a drug or biological target within a target pathway. The benefit of this network brings a perspective to complex diseases which arise from the interaction of many biological networks. 

A recent study has used network-based analysis to model diagnosis progression in human disease. The study created a network using ‘claims data’ which includes diagnostic history of genetic and non-genetic diseases, in addition to risk factors. Claims data also provides chronological medical histories of patients. The constructed network allowed a large-scale analysis of associations between diagnoses, and a better understanding of the relationship across multiple diseases.

Molecular docking

Molecular docking is a useful tool used to quantify the interaction of a protein with a small-molecule ligand in a complex. Studies using molecular docking can determine whether a given drug has the potential to bind to other targets. This method has been exploited to repurpose drugs for different targets including SARS-Cov-2. 

A popular in silico method, molecular docking is a convenient method of rapidly screening extensive libraries of ligands and targets. One of the main drawbacks with molecular docking is ensuring appropriate scoring functions and algorithms are implemented, which could otherwise compromise molecular screening. However, post-processing docking results have been developed to overcome this problem with more accurate scoring functions. 

In silico imaging in clinical trials

While conventional clinical trials can inform whether a product/technology is unsafe or ineffective, they often fail to explain why or how to improve it. In silico clinical trials utilise computer simulation in the development or evaluation of a medical device, intervention or product. This overcomes the challenges of conventional clinical trials by creating algorithms that identify an error or simulating potential improvements.

Imaging in silico clinical trials is a prime example of using computational modelling in biological science. In silico imaging is described as the computational simulation for an entire imaging system. The simulation creates the source, the objection, detection and interpretation that are typically used to evaluate new technologies. The primary endpoint of these clinical trials is to determine the scientific value of imaging technology in comparison to the standard of care. The evaluation of imaging techniques is conducted across three main categories: detection, diagnosis, and guiding/monitoring of disease treatment. In silico imaging in the last decade has further refined R&D, especially in imaging studies like MRIs.

As a computational model, in silico clinical trials require ‘virtual patients’ (VPs). The first step in creating a virtual patient population is using the Virtual Physiological Human (VPH). The VPH is a collective framework that shares resources by many organisations, to integrate computer models of the mechanical, physical, and biochemical functions of a living human body. The VPH is a European enterprise that enables collaborative investigation of the whole human body down to the genomic level. VPs are created by describing the parameters of the target cohort within quantitative VPH models. These models are then encoded with the qualitative information of the human physiology of interest.

VPs offer significant benefits in comparison with human volunteers. In the case of the COVID-19 pandemic, VPs could have predicted whether specific vaccines were likely to work, as well as the potential side effects without the need to test on living candidates, which saves time and cost. 

In silico medicine has shown significant steps in representing the therapeutic response of drugs on virtual organs and body systems. As of now, patients are still needed in late-stage studies to best model tolerability and efficacy. Nevertheless, in silico trials will be able to facilitate faster and more cost-effective risk assessments, reducing the total number of human participants.

How will in silico clinical trials impact the biomedical industry?

Computational-based approaches show promise in optimising drug development and revolutionising clinical research. In silico trials reduce the need for animal models and human cohorts, decreasing the time and cost of studies. Furthermore, the modelling of diseases at a network level enables the personalisation of treatment/device for each patient before needing to be administered/implanted. In silico modelling has the potential to enable precision medicine for complex diseases with variable treatment response across the patient population. 

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Medicinal Chemistry Strategy Meetings, a conference set for 9 May 202.

Charlotte Di Salvo, Junior Medical Writer
Proventa International