Modeling & in silico has been applied to pathophysiology problems to provide information that is not practically available from traditional research methods. It can help better predict pharmacokinetics (PK), drug response, and drug interactions. In silico modeling can save time and money on projects with its better and more efficient study design. BOC Sciences provides modeling & in silico services to explore pharmacology to accelerate drug development programs, providing our clients with reliable results that support the decision-making process.
In silico is commonly used to denote experiments performed by computer and is associated with the more common biological terminology of in vivo and in vitro.
In the field of drug discovery, in silico refers to computational models for studying pharmacological hypotheses using databases, data analysis tools, data mining, homology models, pharmacology, quantitative structure-activity relationships, and other methods. The interaction between a drug and a biological system can be simplified as the action of the biological system on the compound, and the action of the compound on the biological system. The absorption, distribution, metabolism and excretion of drugs by biological systems are pharmacokinetic (PK) events. Drugs acting on biological systems can trigger pharmacological or toxic responses, i.e., pharmacodynamic (PD) events.
Compartmental PK describe the PK of a drug by dividing the entire body into one or more compartments (e.g., blood, organs, and other tissues) and developing models based on nonlinear regression analysis.
PK/PD modeling links pharmacokinetics and pharmacodynamics to establish and evaluate dose-concentration-response relationships and then to describe and predict the effect-time course resulting from drug dose.
Physiological based pharmacokinetic modeling and simulation (PBPK), a computer modeling approach, incorporates blood flow and tissue composition of organs to define the PK of drugs.
Quantitative and/or systems pharmacology (QSP/SP), a modeling approach, integrates preclinical mechanistic evidence and physiological system models to investigate and predict drug response.