Improving the odds of drug development success through human genomics: modelling study
Improving the odds of drug development success through human genomics: modelling study
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DOI：
10.1038/s41598-019-54849-w
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发表时间：
2019-12-11
影响因子：
4.6
通讯作者：
Casas, Juan Pablo
中科院分区：
文献类型：
--
作者：
Hingorani, Aroon D.;Kuan, Valerie;Casas, Juan Pablo
临床阶段药物开发失败的主要原因是在预期的疾病适应症方面缺乏疗效。解释可能包括人类疾病的临床前(细胞、组织和动物)模型的外部有效性较差，以及临床前科学中的高错误发现率(FDR)。FDR与可用于发现的真实关系的比例(伽马)以及为发现它们而设计的实验的类型1(假阳性)和类型2(假阴性)错误率有关。我们估计了临床前科学中的FDR，它对药物开发成功率的影响，以及使用人类基因组学而不是临床前研究作为药物靶标识别的主要证据来源所带来的改善。计算的基础是由所有人类疾病定义的样本空间--以列表示；所有蛋白质编码基因--“蛋白质编码基因组”--以行表示，产生一个独特的基因(或蛋白质)疾病配对矩阵。我们基于10,000种疾病、20,000个蛋白质编码基因、每种疾病100个因果基因和4,000个编码可药物靶标的基因对空间进行了参数化，检查了参数变化和一系列潜在假设对得出的推论的影响。我们估计了伽马，定义了临床前FDR和药物开发成功率之间的数学关系，并基于人类基因组学(而不是正统的临床前研究)估计了成功率的改善。据估计，每200对蛋白质-疾病配对中约有一对是因果的(伽马=0.005)，临床前研究中的FDR值为92.6%，这可能是报告的药物开发失败率96%的主要原因。从样本空间中随机挑选，观察到的成功率仅略高于预期。根据报道的临床前和临床药物开发成功率反算出的伽玛值也接近先验估计。用全基因组(或全基因组)关联研究代替临床前研究作为药物靶点识别的主要信息源，估计可以逆转晚期失败的可能性，因为采用了更严格的类型1错误率，并且能够在同一实验中询问每个潜在的可药物靶点。在更大范围内进行的遗传学研究，如通过在医疗保健系统内连接基因组学和电子健康记录数据，提高疾病终点的分辨率，有可能从根本上提高药物开发的成功率。
Lack of efficacy in the intended disease indication is the major cause of clinical phase drug development failure. Explanations could include the poor external validity of pre-clinical (cell, tissue, and animal) models of human disease and the high false discovery rate (FDR) in preclinical science. FDR is related to the proportion of true relationships available for discovery (gamma), and the type 1 (false-positive) and type 2 (false negative) error rates of the experiments designed to uncover them. We estimated the FDR in preclinical science, its effect on drug development success rates, and improvements expected from use of human genomics rather than preclinical studies as the primary source of evidence for drug target identification. Calculations were based on a sample space defined by all human diseases - the 'disease-ome' - represented as columns; and all protein coding genes - 'the protein-coding genome'-represented as rows, producing a matrix of unique gene- (or protein-) disease pairings. We parameterised the space based on 10,000 diseases, 20,000 protein-coding genes, 100 causal genes per disease and 4000 genes encoding druggable targets, examining the effect of varying the parameters and a range of underlying assumptions, on the inferences drawn. We estimated gamma, defined mathematical relationships between preclinical FDR and drug development success rates, and estimated improvements in success rates based on human genomics (rather than orthodox preclinical studies). Around one in every 200 protein-disease pairings was estimated to be causal (gamma = 0.005) giving an FDR in preclinical research of 92.6%, which likely makes a major contribution to the reported drug development failure rate of 96%. Observed success rate was only slightly greater than expected for a random pick from the sample space. Values for gamma back-calculated from reported preclinical and clinical drug development success rates were also close to the a priori estimates. Substituting genome wide (or druggable genome wide) association studies for preclinical studies as the major information source for drug target identification was estimated to reverse the probability of late stage failure because of the more stringent type 1 error rate employed and the ability to interrogate every potential druggable target in the same experiment. Genetic studies conducted at much larger scale, with greater resolution of disease end-points, e.g. by connecting genomics and electronic health record data within healthcare systems has the potential to produce radical improvement in drug development success rate.