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Model-based development of gemcabene a new lipid-altering agent

机译:基于模型的新型脂质改变剂吉卡宾的开发

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摘要

The purpose of this study was to evaluate the value of model-based, quantitative decision making during the development of gemcabene, a novel lipid-altering agent. The decisions were driven by a model of the likely clinical profile of gemcabene in comparison with its competitors, such as 3-hydroxymethylglutaryl coenzyme A reductase inhibitors (statins), the cholesterol absorption inhibitor ezetimibe, and their combination. Dose-response models were developed for the lipid effects (low-density lipoprotein cholesterol [LDL-C] and high-density lipoprotein cholesterol); adverse effects, such as persistent alanine aminotransferase elevation and myalgia; tolerability issues, such as headache; and risk reduction for coronary artery disease-related events for 5 statins, ezetimibe, gemcabene, and their combinations. The integrated model was based on the joint analysis of publicly available summary-level data and proprietary patient-level data and included information from almost 10,000 patients. The model was made available and accessible to the development team by using the Pharsight Drug Model Explorer model visualization technology. The modeling greatly enhanced the understanding of the clinical profile of gemcabene when given alone or in combination with a statin. The interaction between statins and gemcabene for the LDL-C lowering effect was found to be significantly different from the interaction between statins and ezetimibe. Ezetimibe was found to have a pharmacological-independent interaction resulting in additional LDL-C lowering over the entire statin dose range. The gemcabene interaction was found to be less than independent, resulting in almost no additional LDL-C lowering at high-statin doses, although the drug has a significant LDL-C effect when administered alone or in combination with a low dose of a statin. The quick availability of the model after completion of the first phase II trial in the target patient population and the ability of the team to explore the potential clinical efficacy and safety of gemcabene in comparison with alternative treatment options facilitated a quick decision to stop development.
机译:这项研究的目的是评估新型脂质改变剂吉卡宾的开发过程中基于模型的定量决策的价值。决定的依据是与他们的竞争对手相比,吉卡宾可能具有临床特征的模型,例如3-羟甲基戊二酰辅酶A还原酶抑制剂(他汀类药物),胆固醇吸收抑制剂依泽替米贝及其组合。针对脂质效应(低密度脂蛋白胆固醇[LDL-C]和高密度脂蛋白胆固醇)建立了剂量反应模型;不良反应,如持续的丙氨酸转氨酶升高和肌痛;耐受性问题,例如头痛; 5种他汀类药物,依折麦布,吉卡宾及其组合降低与冠心病相关事件的风险。集成模型基于对公开可用的摘要级别数据和专有患者级别数据的联合分析,并包括来自近10,000名患者的信息。通过使用Pharsight Drug Model Explorer模型可视化技术,该模型可供开发团队使用。当单独或与他汀类药物联合使用时,该模型大大增强了对吉卡宾的临床特征的了解。发现他汀类药物和吉卡宾之间的相互作用降低LDL-C的作用与他汀类药物和依泽替米贝之间的相互作用显着不同。发现依泽替米贝具有非药理学相互作用,导致整个他汀类药物剂量范围内的LDL-C降低。发现吉卡宾的相互作用不那么独立,在高他汀类药物剂量下几乎没有其他LDL-C降低,尽管该药物单独或与低剂量他汀类药物联合给药时具有显着的LDL-C作用。在目标患者人群中完成第一期II期临床试验后,该模型的快速可用性以及与替代治疗方案相比,研究小组探索吉西卡宾的潜在临床疗效和安全性的能力有助于快速决定停止开发。

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