A Novel Concentration-Based Time-Imputation Algorithm for Large Cohort Studies Missing Time-After-Dose Data for Stored Samples
Authors: Khoei Adrian , Edmonds Andrew , Shook-Sa Bonnie , Chang Yen , Cole Stephen , Lahiri Cecile , Tien Phyllis , Ofotokun Igho , Kempf Mirjam-Colette , Alcaide Maria , French Audrey , Margolick Joseph , Brown Todd , Anastos Kathryn , Ho Ken , DeHovitz Jack , Gange Stephen , Merenstein Daniel , Tamraz Bani , Aouizerat Bradley , Sheth Anandi , Wolinsky Steven , Dumond Julie 
Published: February 2025

OBJECTIVES: Longitudinal studies with biospecimen collection are rich for pharmacokinetics (PK)-adverse effects analyses but may lack time-after-dose (TAD). Using dolutegravir (DTG) PK and TAD in women (Women’s Interagency HIV Study; WIHS), we developed a novel method to infer TAD in men (Multicenter AIDS Cohort Study; MACS), which never collected TAD. METHODS: We observed a trimodal TAD distribution in WIHS women on DTG, with each of the 3 components characterized by mean, scale, and shape parameters of a skew-normal distribution. We assumed a similar distribution in MACS and verified it by comparing TAD in WIHS to TAD in MACS/WIHS Combined Cohort Study (MWCCS), which has collected TAD since 2020, with most participants originally from WIHS or MACS. We developed an algorithm inspired by the k-nearest neighbors algorithm and multiple imputation methods to impute TAD in MACS using WIHS (R v.4.3.2). Our algorithm assigned each MACS timeless DTG concentration to one of the WIHS TAD components by sampling from the predicted population-level distribution of WIHS DTG concentrations at each component’s mode. It then picked the component where the sampled concentration was closest in absolute distance to the timeless concentration, using a correction factor inversely proportional to the number of observations per component. A time was then sampled from the chosen component’s TAD distribution and paired with the timeless concentration. This process repeated for all concentrations over 1000 iterations, creating 1000 imputed datasets, which were fit in NONMEM using a 1-compartment PK model. Population-level PK parameters of 1000 estimates were summarized as median and median absolute deviation (MAD). RESULTS: Like WIHS, we observed a trimodal TAD distribution in MWCCS participants regardless of their HIV regimen, with 3 TAD modes reflecting 3 trends: 2 hrs (AM dose/AM visit), 13.5 hrs (PM dose/AM visit), and 24.5 hrs (AM dose on the day prior). For internal validation of algorithm performance, we used WIHS data for training and testing. The WIHS population estimate and 1000 estimates’ medians (MAD; % change) for clearance, volume of distribution, and absorption rate constant were 1.27 vs. 1.27 L/h (0.01; 0%), 36 vs. 30 L (3.3; -16%), and 1.23 vs. 1 h-1 (0.32; -23%), respectively. For external validation, we used WIHS for training and a Phase 1 DTG PK study in men [1] for testing. The corresponding values for the external dataset were 0.94 vs. 1.14 L/h (0.04; +20%), 20.8 vs. 20.8 L (2.3; 0%), and 1.91 vs. 2 h-1 (1.4; +4.5%). CONCLUSIONS: We observed a trimodal TAD distribution in WIHS and MWCCS that may apply to similar studies with daily dosing. Our method leverages known PK to predict TAD and allows the use of data with missing TAD. Future work includes adding a drug taken with DTG to the algorithm and exploring alternative statistical methods. Our goal is to estimate AUCs in MACS to analyze the causal effects of DTG exposure on cardiometabolic outcomes.Citations: [1] Greener et al., J Acquir Immune Defic Syndr. 2013;64(1):39-44. PMID: 23945251