Enabling comprehensive assessments of drug-drug interaction risks
Dr. Kevin Lustig, President and CEO of The Assay Depot, Inc. and Dr. Maria Thompson, Principal Consultant use PharmaPendium for DDI assessments
Dr. Kevin Lustig, President and CEO of The Assay Depot, Inc., and Dr. Maria Thompson, Principal Consultant, look at the dynamics of drug metabolizing enzymes and transporters and how an understanding of this critical aspect of biology enables assessments of drug-drug interaction risks. In addition, they comment on PharmaPendium, which includes the DMPK solution with its DDI Risk Calculator, as an important tool in making these assessments successfully.
Understanding the dynamics of drug metabolizing enzymes and transporters (METs) is critical to successful drug development. MET behavior can significantly impact drug safety. Many factors regulate enzymes and transporters, which in turn affect the pharmacokinetic properties of drugs and mediate drug interactions. Evaluation of drug interaction profiles require detailed in vitro and in vivo studies along with predictive modeling.
PharmaPendium offers a unique platform for modeling advanced drug interactions. PharmaPendium’s DMPK Solution, which includes a powerful drug-drug interaction risk calculator, provides access to preclinical and clinical data on drug metabolizing enzymes and transporters, in addition to in-depth pharmacokinetic parameters on all approved drugs. This helps scientists to identify drug-drug interactions and to make informed decisions in drug candidate assessment so that they can prioritize the safest, most promising candidates for further development.
Instead of spending weeks gathering data that we’d then have to standardize, we now spend half a day on PharmaPendium.
Dr. Kevin Lustig, President & CEO The Assay Depot Inc.
Access to the MET data and the ability to predict drug-drug interactions is incredibly valuable for optimized research workflows and accelerated drug development. They enable intelligent, in silico drug design and provide scientists with comprehensive knowledge about drug metabolism, significantly reducing the need for costly and time-consuming lab work and animal models and informing critical decisions on clinical trial design. Additionally, this solution sheds light on past regulatory issues, providing an opportunity to avoid pitfalls and streamline the regulatory submission process.
This wealth of detailed, filterable data can guide drug development decisions, avoiding costly late-stage drug failures.
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