Drug metabolism research plays a key role in drug discovery and development. Based on the discovery of drug metabolites, new chemical entities can be identified and potential safety concerns caused by reactive or toxic metabolites can be minimized. With years of experience, we provide customized computer prediction of drug metabolism service to precisely meet customer requirements.

Introduction of Computer Prediction of Drug Metabolism

Small molecule drug discovery is time-consuming and expensive, yet up to 25% of candidates fail clinical trials due to metabolic, pharmacokinetic or toxicity issues. Drug metabolism can produce metabolites with physicochemical and pharmacological properties, and the metabolic transformations that occur in the body can alter their bioavailability, efficacy, chronic toxicity, excretion rates and pathways. Studies have found that 70% of clinical drugs are eliminated by the human metabolic system, so understanding the metabolism of drugs is critical to the success of drug discovery and development. Many experimental methods are available to explore the metabolic processes of drugs, however experimental methods are still demanding in terms of equipment, expertise, cost, and time. Therefore, developing computational tools for predicting drug metabolism holds great promise for lower cost and higher throughput.

Fig 1. Metabolic reaction product prediction flow chart. (Wang D, et al., 2020)

Services

Sites of metabolism (SOMs) and metabolite structures are the two main research directions of computer-aided metabolic prediction methods, which can provide decisive support and guidance for experimenters. Creative BioMart is committed to using a variety of computational methods to predict drug metabolism to assist in drug discovery and development, with important implications for optimizing drug stability and in vivo half-life. Our service supports more reliable metabolite predictions calculated from input structures alone, and has been widely used to predict some major metabolic pathways and identify the involvement of P450s. In addition, we have evaluated several classification models that use more than 300 molecules and their metabolites to predict metabolic responses.

Improving the pharmacokinetic profile of compounds by altering metabolic susceptibility is an important step. Our scientists have now extensively studied the specific interactions of P450-substrate/inhibitor recognition and have established several QSAR and pharmacophore models for a limited number of these enzymes. Computational prediction techniques for human drug metabolism based on databases, QSMR/QSAR, pharmacophores, rule-based methods, electronic models, homology models, and crystal structures have achieved varying degrees of success.

Approaches of Computer Prediction of Drug Metabolism

We employ the following in silico methods for computer-aided prediction of drug toxicity and metabolism:

• Database-based methods: these databases can be used to calculate the probability of a given metabolic response and indicate possible metabolites or metabolic sites by statistical methods.
• Rule-based method: this approach uses data mining techniques. Large databases with metabolic data are used to extract general rules to determine the fraction of molecules that undergo metabolic alterations.
• Descriptor-based method: this approach relies on the assumption that the metabolic fate of compounds is entirely determined by their chemical structure and properties.

Creative BioMart is committed to providing global customers with high-quality computer prediction of drug metabolism service to predict drug metabolism, including determination of enzymes involved, metabolic sites, metabolites produced, and metabolic rates. We will work with you to develop the most appropriate strategy and provide the most meaningful data for your research for accelerating the research of life sciences. If you are interested in our services, please do not hesitate to contact us for more information.

References

1. Ekins, S. (2006). Computer methods for predicting drug metabolism. In Computer applications in pharmaceutical research and development. John Wiley and Sons Hoboken, 445-468.
2. Wang D, Liu W, Shen Z, et al.. (2020) Deep Learning Based Drug Metabolites Prediction. Front Pharmacol. 10:1586.

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