Schuhmacher Alexander, Brieke Clara, Gassmann Oliver, Hinder Markus, Hartl Dominik
Reutlingen University, Alteburgstrasse 150, D-72762 Reutlingen, Germany; University of St. Gallen, Institute of Technology Management, Dufourstrasse 40a, CH-9000 St. Gallen, Switzerland.
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Jahnstrasse 29, D-69120 Heidelberg, Germany.
Drug Discov Today. 2021 Dec;26(12):2786-2793. doi: 10.1016/j.drudis.2021.06.015. Epub 2021 Jul 3.
Delivering transformative therapies to patients while maintaining growth in the pharmaceutical industry requires an efficient use of research and development (R&D) resources and technologies to develop high-impact new molecular entities (NMEs). However, increasing global R&D competition in the pharmaceutical industry, growing impact of generics and biosimilars, more stringent regulatory requirements, as well as cost-constrained reimbursement frameworks challenge current business models of leading pharmaceutical companies. Big data-based analytics and artificial intelligence (AI) approaches have disrupted various industries and are having an increasing impact in the biopharmaceutical industry, with the promise to improve and accelerate biopharmaceutical R&D processes. Here, we systematically analyze, identify, assess, and categorize key risks across the drug discovery and development value chain using a new risk map approach, providing a comprehensive risk-reward analysis for pharmaceutical R&D.
在制药行业保持增长的同时为患者提供变革性疗法，需要有效利用研发资源和技术来开发具有高影响力的新分子实体（NME）。然而，制药行业全球研发竞争加剧、仿制药和生物类似药的影响日益增大、监管要求更加严格，以及成本受限的报销框架，都对领先制药公司当前的商业模式构成挑战。基于大数据的分析和人工智能（AI）方法已颠覆了各个行业，并且在生物制药行业的影响越来越大，有望改善和加速生物制药研发流程。在此，我们使用一种新的风险地图方法，对药物发现和开发价值链中的关键风险进行系统分析、识别、评估和分类，为制药研发提供全面的风险回报分析。
