Novel Dual-Stage Antimalarials: Machine learning prediction, validation and evolution

项目总结 具体地说，这项建议侧重于抑制血液和肝脏的新的新小分子 疟疾感染的各个阶段。病原菌--疟原虫。-负责241,000,000起案件 这导致2020年有62.7万人死亡。疟原虫属耐药感染给患者留下的好选择不多 并将感染者的生产力和生命置于危险之中。已经有了明确的理由支持新的 通过发现和开发新的治疗策略来治疗这些感染的药物。这些 策略最好是两个阶段，以血液阶段为治疗目标，以肝脏阶段为目标 预防措施。 本提案中的创新战略建立在机器学习模型技术的基础上， 具有重大药物发现实体潜力的新型两阶段抗疟疾小分子的预测。 这种计算方法为发现小分子疟疾寄生虫抑制剂提供了双重- 我们在2022年才报告了阶段疗效。该方法首先从两部小说的初步数据开始 体外抗疟小分子对血期和肝期疟原虫的疗效 SPP.对培养的肝细胞没有明显的细胞毒性。这些分子是从一种 使用高通量筛选数据训练的随机森林模型发现的一组命中。这些分子 是两阶段抗疟疾药物的新型化学类型的代表，因此提供了很高的可能性 调节在寄生虫整个生命周期中至关重要的新靶点。这一最初的机器学习努力将 通过一系列模型类型和不同且更大的商业库显著扩展，以预测 一系列新的热门化合物。 两种有效的HIT，符合体外疗效和细胞毒性标准，并在体外保持野生型 对一组抗药性寄生虫菌株的疗效将被描述为关键的分子特性，如小鼠 肝微粒体稳定性、水溶度和小鼠药代动力学特征。这些数据以及 现有的体外疗效和细胞毒性评估将指导每一种HIT的进化，目标是准备 一种或多种具有复合配置文件的类似物，以实现体内感染疗效的下游评估 模特们。药物化学和机器学习的新组合将被用来负担这样的费用 分子。

PROJECT SUMMARY Specifically, this proposal focuses on novel new small molecules that inhibit both the blood and liver stages of malaria infection. The causative pathogen – Plasmodium spp. – was responsible for 241,000,000 cases that resulted in 627,000 deaths in 2020. Plasmodium spp. drug-resistant infections leave few good choices for physicians and put at risk the productivity and the lives of those infected. A clear case has been made for new drugs to treat these infections through the discovery and development of novel therapeutic strategies. These strategies would optimally be dual stage, targeting the blood stage for treatment and the liver stage for prophylaxis. The innovative strategy in this proposal builds on the technology of machine learning models for the prediction of novel dual-stage antimalarial small molecules with significant potential as drug discovery entities. Such a computational approach to seed the discovery of small molecule malaria parasite inhibitors with dual- stage efficacy has only been reported by us in 2022. The approach begins with preliminary data around two novel antimalarial small molecules with demonstrated in vitro efficacy versus both blood and liver stages of Plasmodium spp. infection and a lack of significant cytotoxicity to cultured liver cells. These molecules were derived from a set of hits discovered with a random forest model trained with high-throughput screening data. The molecules are representative of novel chemotypes for dual-stage antimalarials and, thus, offer a high probability of modulating new targets that are critical throughout the parasite’s lifecycle. This initial machine learning effort will be significantly expanded with a range of model types and a different and larger commercial library to predict a set of new hit compounds. Two validated hits, meeting in vitro efficacy and cytotoxicity criteria and maintaining wild type in vitro efficacy versus a set of drug-resistant parasite strains, will be profiled for key molecular properties such as mouse liver microsomal stability, aqueous solubility, and mouse pharmacokinetic profile. These data along with the existing in vitro efficacy and cytotoxicity evaluations will guide the evolution of each hit with a goal of preparing one or more analogs with a composite profile to enable downstream in vivo efficacy evaluation in infection models. A novel combination of medicinal chemistry and machine learning will be leveraged to afford such molecules.