Department of Systems Biology and Engineering, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland.
Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014, Helsinki, Finland.
Sci Rep. 2024 Nov 29;14(1):29721. doi: 10.1038/s41598-024-80768-6.
Clinical trials are costly and time-intensive endeavors, with a high rate of drug candidate failures. Moreover, the standard approaches often evaluate drugs under a limited number of protocols. In oncology, where multiple treatment protocols can yield divergent outcomes, addressing this issue is crucial. Here, we present a computational framework that simulates clinical trials through a combination of mathematical and statistical models. This approach offers a means to explore diverse treatment protocols efficiently and identify optimal strategies for oncological drug administration. We developed a computational framework with a stochastic mathematical model as its core, capable of simulating virtual clinical trials closely recapitulating the clinical scenarios. Testing our framework on the landmark SOLO-1 clinical trial investigating Poly-ADP-Ribose Polymerase maintenance treatment in high-grade serous ovarian cancer, we demonstrate that managing toxicity through treatment interruptions or dose reductions does not compromise treatment's clinical benefits. Additionally, we provide evidence suggesting that further reduction of hematological toxicity could significantly improve the clinical outcomes. The value of this computational framework lies in its ability to expedite the exploration of new treatment protocols, delivering critical insights pivotal to shaping the landscape of upcoming clinical trials.
临床试验是一项昂贵且耗时的工作，候选药物的失败率很高。此外，标准方法通常在有限数量的方案下评估药物。在肿瘤学中，多种治疗方案可能会产生不同的结果，解决这个问题至关重要。在这里，我们提出了一个通过数学和统计模型相结合来模拟临床试验的计算框架。这种方法提供了一种有效的方法来探索不同的治疗方案，并为肿瘤药物管理确定最佳策略。我们开发了一个计算框架，其核心是一个随机数学模型，能够模拟虚拟临床试验，紧密再现临床场景。我们在里程碑式的 SOLO-1 临床试验中对该框架进行了测试，该试验研究了聚 ADP-核糖聚合酶在高级别浆液性卵巢癌中的维持治疗，结果表明，通过中断治疗或减少剂量来控制毒性不会影响治疗的临床获益。此外，我们提供的证据表明，进一步降低血液学毒性可以显著改善临床结果。该计算框架的价值在于它能够加快探索新的治疗方案的速度，提供关键的见解，这对于塑造即将到来的临床试验格局至关重要。
