Toward the accurate prediction of P450-mediated metabolism and adverse drug reactions using novel MM methods and QM-derived rules.

尽管在毒理学和候选药物的临床试验方面投入了大量资金，但药物不良反应仍然是药物停药的主要原因。这种形式的药物毒性可能只影响一小部分患者，在临床试验中未被发现。然而，如果药物已经在市场上广泛使用，这种毒性可能是致命的。多年来，药物化学家一直依靠“结构警报”来识别药物的潜在毒性和更具体的特异性毒性。这些官能团通常被代谢酶（如P450）激活为反应性代谢物，导致观察到的毒性。然而，这种官能团的存在并不总是与生物活性的毒性相关

Despite the large investment in toxicology and clinical trials of drug candidates, adverse drug reactions remain a leading cause of drug withdrawal. This form of toxicity of drugs may only affect a small fraction of the patients and is not detected in clinical trials. However, if the drug is already on the market and widely used this toxicity may be fatal. Over the years, medicinal chemists have relied on "structural alerts" to identify potential toxicity of drugs and more specifically idiosyncratic toxicity. These functional groups are often activated by metabolic enzymes (e.g., P450) into reactive metabolites leading to the observed toxicity. However the presence of such a functional group does not always correlate with toxicity as the bioactivation can only occur if the drug is processed (i.e., recognized) by these enzymes and although widely used this approach is not reliable (C&EN 2012, 90, 34). Thus, computational prediction has become an avenue of research and in this context we have developed a program, IMPACTS (AstraZeneca/NSERC CRD funding 01/2010-02/2012) which predicts the site of metabolism of drugs, hence whether the drug and structural alert can be processed by the major P450s. We next decided to further improve this program in two ways. On one side, we thought to use quantum mechanical techniques to improve the ligand reactivity rules and more specifically their inclination to lead to reactive metabolites once bioactivated by P450s focusing on benzene derivatives. Second we investigated the development of a conceptually novel force field to compute drug potential energies more accurately. In a proof of concept study (Chemical Computing Group/NSERC CRD 08/2013-08/2014), we have shown that these two optimizations can indeed be possible. In the current proposal, we propose to further work on these two front and complete the development of a program predicting reactive metabolites hence, adverse drug reactions.