Schomaker Michael, Denti Paolo, Bienczak Andrzej, Burger David, Díaz Iván, Gibb Diana M, Walker Ann Sarah, McIlleron Helen
Department of Statistics, Ludwig-Maximilians Universität München, München, Germany.
Centre for Infectious Disease Epidemiology and Research, School of Public Health, University of Cape Town, Cape Town, South Africa.
Pharmacoepidemiol Drug Saf. 2024 Dec;33(12):e70051. doi: 10.1002/pds.70051.
Determining a therapeutic window for maintaining antiretroviral drug concentrations within an appropriate range is required for identifying effective dosing regimens. The limits of this window are typically calculated using predictive models. We propose that target concentrations should instead be calculated based on counterfactual probabilities of relevant outcomes and describe a counterfactual framework for this.
The proposed framework is applied in an analysis including longitudinal observational data from 125 HIV-positive children treated with efavirenz-based regimens within the CHAPAS-3 trial, which enrolled children < 13 years in Zambia/Uganda. A directed acyclic graph was developed to visualize the mechanisms affecting antiretroviral concentrations. Causal concentration-response curves, adjusted for measured time-varying confounding of weight and adherence, are calculated using g-computation.
The estimated curves show that higher concentrations during follow-up, 12/24 h after dose, lead to lower probabilities of viral failure (> 100 c/mL) at 96 weeks of follow-up. Estimated counterfactual failure probabilities under the current target range of 1-4 mg/L range from 24% to about 2%. The curves are almost identical for slow, intermediate and extensive metabolizers and show that a mid-dose concentration level of ≥ 3.5 mg/L would be required to achieve a failure probability of < 5%.
Our analyses demonstrate that a causal approach may lead to different minimum concentration limits than analyses that are based on purely predictive models. Moreover, the approach highlights that indirect causes of failure, such as patients' metabolizing status, may predict patients' failure risk, but do not alter the threshold at which antiviral activity of efavirenz is severely reduced.
确定一个将抗逆转录病毒药物浓度维持在适当范围内的治疗窗，对于确定有效的给药方案至关重要。该治疗窗的界限通常使用预测模型来计算。我们建议，目标浓度应基于相关结局的反事实概率来计算，并为此描述一个反事实框架。
将所提出的框架应用于一项分析，该分析纳入了CHAPAS-3试验中125名接受基于依非韦伦方案治疗的HIV阳性儿童的纵向观察数据，该试验在赞比亚/乌干达招募了13岁以下的儿童。绘制了一个有向无环图，以可视化影响抗逆转录病毒药物浓度的机制。使用g计算法计算经体重和依从性等随时间变化的测量混杂因素调整后的因果浓度-反应曲线。
估计曲线显示，随访期间（给药后12/24小时）浓度越高，随访96周时病毒学失败（>100 c/mL）的概率越低。在当前1-4mg/L的目标范围内，估计的反事实失败概率在24%至约2%之间。慢代谢者、中代谢者和快代谢者的曲线几乎相同，表明要使失败概率<5%，需要达到≥3.5mg/L的中剂量浓度水平。
我们的分析表明，与基于纯预测模型的分析相比，因果方法可能导致不同的最低浓度界限。此外，该方法强调，失败的间接原因，如患者的代谢状态，可能预测患者的失败风险，但不会改变依非韦伦抗病毒活性严重降低的阈值。
