Precision Dosing in Presence of Multiobjective Therapies by Integrating Reinforcement Learning and PK-PD Models: Application to Givinostat Treatment of Polycythemia Vera.

IF 3.1 3区 医学 Q2 PHARMACOLOGY & PHARMACY
Alessandro De Carlo, Elena Maria Tosca, Paolo Magni
{"title":"Precision Dosing in Presence of Multiobjective Therapies by Integrating Reinforcement Learning and PK-PD Models: Application to Givinostat Treatment of Polycythemia Vera.","authors":"Alessandro De Carlo, Elena Maria Tosca, Paolo Magni","doi":"10.1002/psp4.70012","DOIUrl":null,"url":null,"abstract":"<p><p>Precision dosing aims to optimize and customize pharmacological treatment at the individual level. The integration of pharmacometric models with Reinforcement Learning (RL) algorithms is currently under investigation to support the personalization of adaptive dosing therapies. In this study, this hybrid technique is applied to the real multiobjective precision dosing problem of givinostat treatment in polycythemia vera (PV) patients. PV is a chronic myeloproliferative disease with an overproduction of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). The therapeutic goal is to simultaneously normalize the levels of these efficacy/safety biomarkers, thus inducing a complete hematological response (CHR). An RL algorithm, Q-Learning (QL), was integrated with a PK-PD model describing the givinostat effect on PLT, WBC, and HCT to derive both an adaptive dosing protocol (QL<sub>pop</sub>-agent) for the whole population and personalized dosing strategies by coupling a specific QL-agent to each patient (QL<sub>ind</sub>-agents). QL<sub>pop</sub>-agent learned a general adaptive dosing protocol that achieved a similar CHR rate (77% vs. 83%) when compared to the actual givinostat clinical protocol on 10 simulated populations. Treatment efficacy and safety increased with a deeper dosing personalization by QL<sub>ind</sub>-agents. These QL-based patient-specific adaptive dosing rules outperformed both the clinical protocol and QL<sub>pop</sub>-agent by reaching the CHR in 93% of the test patients and completely avoided severe toxicities during the whole treatment period. These results confirm that RL and PK-PD models can be valid tools for supporting adaptive dosing strategies as interesting performances were achieved in both learning a general set of rules and in customizing treatment for each patient.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CPT: Pharmacometrics & Systems Pharmacology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/psp4.70012","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0

Abstract

Precision dosing aims to optimize and customize pharmacological treatment at the individual level. The integration of pharmacometric models with Reinforcement Learning (RL) algorithms is currently under investigation to support the personalization of adaptive dosing therapies. In this study, this hybrid technique is applied to the real multiobjective precision dosing problem of givinostat treatment in polycythemia vera (PV) patients. PV is a chronic myeloproliferative disease with an overproduction of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). The therapeutic goal is to simultaneously normalize the levels of these efficacy/safety biomarkers, thus inducing a complete hematological response (CHR). An RL algorithm, Q-Learning (QL), was integrated with a PK-PD model describing the givinostat effect on PLT, WBC, and HCT to derive both an adaptive dosing protocol (QLpop-agent) for the whole population and personalized dosing strategies by coupling a specific QL-agent to each patient (QLind-agents). QLpop-agent learned a general adaptive dosing protocol that achieved a similar CHR rate (77% vs. 83%) when compared to the actual givinostat clinical protocol on 10 simulated populations. Treatment efficacy and safety increased with a deeper dosing personalization by QLind-agents. These QL-based patient-specific adaptive dosing rules outperformed both the clinical protocol and QLpop-agent by reaching the CHR in 93% of the test patients and completely avoided severe toxicities during the whole treatment period. These results confirm that RL and PK-PD models can be valid tools for supporting adaptive dosing strategies as interesting performances were achieved in both learning a general set of rules and in customizing treatment for each patient.

整合强化学习和PK-PD模型的多目标治疗精准给药:在给予维诺他治疗真性红细胞增多症中的应用。
精确给药的目的是在个体水平上优化和定制药物治疗。目前正在研究将药物计量模型与强化学习(RL)算法相结合,以支持适应性给药治疗的个性化。在这项研究中,这种混合技术被应用于真性红细胞增多症(PV)患者给予他汀治疗的多目标精确给药问题。PV是一种慢性骨髓增生性疾病,伴有血小板(PLT)、白细胞(WBC)和红细胞压积(HCT)的过度产生。治疗目标是同时使这些疗效/安全性生物标志物的水平正常化,从而诱导完全血液学反应(CHR)。将RL算法Q-Learning (QL)与描述给维他汀对PLT、WBC和HCT影响的PK-PD模型相结合,得出适用于整个人群的自适应给药方案(QLpop-agent)和通过将特定的QL-agent耦合到每个患者的个性化给药策略(QLind-agents)。qpop -agent学习了一种通用的自适应给药方案,与10个模拟人群的实际给予维司他临床方案相比,该方案实现了相似的CHR率(77%对83%)。随着QLind-agents给药个性化程度的加深,治疗效果和安全性也随之提高。这些基于ql的患者特异性适应性给药规则优于临床方案和QLpop-agent,在93%的测试患者中达到了CHR,并且在整个治疗期间完全避免了严重的毒性。这些结果证实,RL和PK-PD模型可以作为支持自适应给药策略的有效工具,因为在学习一般规则集和为每个患者定制治疗方面都取得了有趣的表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
5.00
自引率
11.40%
发文量
146
审稿时长
8 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信