When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis
When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis
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DOI：
10.1371/journal.pone.0147215
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发表时间：
2016-02-10
期刊：
影响因子：
3.7
通讯作者：
Bosley, Jim
中科院分区：
文献类型：
--
作者：
Scannell, Jack W.;Bosley, Jim
在过去的60年里，生物制药的发现、研究和发展一直是一个鲜明的对比。巨大的科技进步本应提高学术科学质量，提高产业研发效率。然而，学术界面临着“可再现性危机”；1950年至2010年间，每种新药的工业研发成本经通胀调整后增长了近100倍；与20世纪70年代相比，今天的药物更有可能在临床开发中失败。只有在强大的逆风逆转了收益和/或许多“收益”被证明是虚幻的情况下，这种反差才可以解释。然而，关于可重复性和研发生产率的讨论很少明确地解决这一点。本文初步研究的主要目标是：(a)为这种对比提供定量和历史上合理的解释；(b)识别研发效率的敏感因素。本文提出了研发过程的定量决策理论模型。该模型表示“测量空间”内的候选治疗药物（例如，假定的药物靶点，筛选文库中的分子等），候选药物的位置取决于它们在各种检测中的表现（例如，结合亲和力，毒性，体内功效等），其结果或多或少地相关。我们应用决策规则来分割空间，并评估正确研发决策的概率。我们发现，在寻找罕见的阳性结果（例如，将成功完成临床开发的候选结果）时，筛选和疾病模型的预测有效性的变化(许多从事药物发现工作的人会认为这些变化很小和/或不可知（即，模型输出与人体临床结果之间的相关系数的绝对变化为0.1）可以抵消模型的强力效率的巨大变化（例如，10倍，甚至100倍）。我们还展示了有效性和可重复性如何在模拟筛选和疾病模型的人群中相互关联。我们假设，具有高预测有效性的筛查和疾病模型更有可能产生好的答案和好的治疗方法，因此往往会使自己和他们的疾病在学术和商业上变得多余。也许也有过于热情的还原论分子模型，没有足够的预测有效性。因此，我们假设，随着时间的推移，学术上和工业上“有趣的”筛查和疾病模型的平均预测有效性已经下降，即使是很小的下降也能抵消科学知识和蛮力效率方面的巨大收益。有效的筛选和疾病模型的创造速度可能是研发生产力的主要制约因素。
A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a "reproducibility crisis"; inflation-adjusted industrial R&D costs per novel drug increased nearly 100 fold between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s. The contrast is explicable only if powerful headwinds reversed the gains and/or if many "gains" have proved illusory. However, discussions of reproducibility and R&D productivity rarely address this point explicitly. The main objectives of the primary research in this paper are: (a) to provide quantitatively and historically plausible explanations of the contrast; and (b) identify factors to which R&D efficiency is sensitive. We present a quantitative decision-theoretic model of the R&D process. The model represents therapeutic candidates (e.g., putative drug targets, molecules in a screening library, etc.) within a "measurement space", with candidates' positions determined by their performance on a variety of assays (e.g., binding affinity, toxicity, in vivo efficacy, etc.) whose results correlate to a greater or lesser degree. We apply decision rules to segment the space, and assess the probability of correct R&D decisions. We find that when searching for rare positives (e.g., candidates that will successfully complete clinical development), changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (i.e., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man) can offset large (e.g., 10 fold, even 100 fold) changes in models' brute-force efficiency. We also show how validity and reproducibility correlate across a population of simulated screening and disease models. We hypothesize that screening and disease models with high predictive validity are more likely to yield good answers and good treatments, so tend to render themselves and their diseases academically and commercially redundant. Perhaps there has also been too much enthusiasm for reductionist molecular models which have insufficient predictive validity. Thus we hypothesize that the average predictive validity of the stock of academically and industrially "interesting" screening and disease models has declined over time, with even small falls able to offset large gains in scientific knowledge and brute-force efficiency. The rate of creation of valid screening and disease models may be the major constraint on R&D productivity.