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，在临床试验中没有检测到。然而，如果这种药物已经上市并广泛使用，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，更具体地说，是特殊毒性。这些官能团通常被代谢酶(如P450)激活为反应性代谢物，从而导致观察到的毒性。然而，这种官能团的存在并不总是与生物活化的毒性相关 只有当药物被这些酶加工(即识别)时才能发生，尽管这种方法被广泛使用，但这种方法并不可靠(C&EN 2012，90，34)。因此，计算预测已成为一种研究途径，在此背景下，我们开发了一个名为Impact的程序(AstraZeneca/NSERC CRD Funding 01/2010-02/2012)，该程序预测药物的新陈代谢位置，从而预测药物和结构警报是否可以被主要的P450处理。接下来，我们决定从两个方面进一步改进这一计划。一方面，我们认为使用量子力学技术来改善配体的反应规则，更具体地说，他们倾向于导致活性代谢物一旦被P450生物激活，重点放在苯衍生物上。其次，我们研究了一种概念上新颖的力场的发展，以更准确地计算药物势能。在一项概念验证研究(化学计算组/NSERC CRD 08/2013-08/2014)中，我们已经证明了这两个优化确实是可能的。在目前的提案中，我们建议在这两个方面进一步工作，并完成预测反应性代谢物从而导致药物不良反应的程序的开发。

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.