An in silico model is a computer program that is designed to simulate an experiment. It can be used to simulate drug effects, physiology or other aspects of health.
The use of these models is growing steadily as computer power increases. But they still face a number of challenges.
What is an in silico model?
An in silico model is a computational simulation of a complex system. It can be used for various purposes, including preclinical research and pharmacokinetic modelling.
In silico models can simulate complex biological processes with high fidelity. However, these models need to be validated by conventional laboratory experiments.
To develop in silico models, researchers need to be well-versed in biology and mathematics. They also need to be able to communicate effectively with others involved in the process.
A common use for in silico model development is to predict drug-drug interactions (DDIs) based on in vitro data. These models can be used to assess a drug’s potential for DDI-related hazards and help select a first-in-human dose.
In silico models can be developed using different techniques, including cellular automata and partial differential equations. They can be used to model a wide range of biological phenomena, from cytotoxic and apoptosis effects to the interaction between cancer cells and the immune system.
What are the advantages of using an in silico model?
In silico models have the potential to accelerate drug development by bypassing expensive and time-consuming conventional preclinical research. They also allow scientists to investigate complex biological processes with high fidelity and flexibility.
Various physicochemical properties of drug molecules and nanoparticles, such as activity spectra, pharmacokinetics, transport and toxicity, can be predicted by in silico models. These can then be used to identify therapeutic targets and optimise compounds prior to IND submissions.
For example, a permeability-limited pharmacokinetic model can be used to predict drug delivery through the body. This is particularly useful when assessing the permeability of biological membranes for small molecule and nanoparticle drugs.
Similarly, in silico DDI prediction models extrapolate in vitro data to clinically relevant enzyme- or transporter-mediated drug interactions. They can aid with the selection of first-in-human (FIH) dose by predicting safe drug concentrations. These model-based methods are a cost-effective alternative to costly in vivo DDI studies and can be used to help sponsors meet the latest FDA Drug Interaction guidance documents.
What are the disadvantages of using an in silico model?
In silico model are a promising approach for drug discovery and development. They are also a cost-effective alternative to animal experiments and facilitate translation to human. However, these models are not perfect & their accuracy is limited.
One of the main disadvantages is that they do not capture all of a cell’s behaviour. For example, they do not include all the factors determining whether a cell will move from one location to another.
Therefore, there is no guarantee that the model will accurately predict how a cell will react to different drug concentrations or other treatments. It also does not reflect all of the key biomarkers, such as protein folding and oxidative stress.
In this study, we have investigated the ability of in silico drug trials to predict clinical risk of drug-induced arrhythmias based on ion channel information and to identify ionic profiles that increase the risk of repolarization abnormalities (Figure 1). These results are ready for integration into the existing drug safety assessment pipelines and could reduce the need for animal experiments in the near future.
What are the possible applications of an in silico model?
In silico models could potentially accelerate drug discovery by circumventing the need for expensive and time-consuming conventional preclinical research conducted in animal or in vitro models. This would enable more rapid development of novel drug molecules, or predict the effects of functional gene polymorphisms and therapeutic combinations.
There are several different in silico methods used for this purpose. These include bacterial sequencing techniques, protein-ligand docking algorithms, and chemical structures based on expert systems.
Another family of in silico methods includes physiologically-based biokinetic (PBBK) modelling, which is increasingly used to predict the toxic effects of compounds. PBBK models are able to estimate the effective toxic concentration in a particular tissue, given a certain dose or exposure pattern.
In addition to these types of model, non-testing methods such as grouping approaches and structure-activity relationships are also increasingly being used in the field of toxicology. They are not currently widely used in pharmaceutical research and development, but they are expected to play a more prominent role in the future.