The number of poorly water-soluble drugs in the pipeline has increased, and they are often not well absorbed by the gastrointestinal tract. Amorphous solid dispersion (ASD) is an emerging strategy to improve drug solubility and absorption. The overall aim is to expand methods used to evaluate the performance of ASD as a strategy to improve water solubility. Firstly, we aimed to develop an in vitro-in vivo correlation (IVIVC) model to predict human pharmacokinetics (PK) of itraconazole tablets with different release rates from dissolution experiments and determine formulation and process parameters that affect in vivo performance. Human PK was successfully predicted from in vitro dissolution experiments, and the IVIVC model created here met internal predictability criteria. Secondly, liquid state proton nuclear magnetic resonance (1HNMR) techniques were used to streamline polymer selection for ASDs in a non-destructive and resource-sparing fashion. For three drug-polymer pairs (i.e. etravirine with each HPMC, HPMCAS-M, and PVP-VA), 1HNMR findings were compared to supersaturation studies. Our hypothesis was that strong molecular interactions between polymer and drug observed in 1HNMR predicted precipitation kinetics in the supersaturation studies. Supersaturation studies agreed with 1HNMR predictions, as HPMC and HPMCAS-M maintained etravirine in solution for a longer time than PVP-VA. Thirdly, a robust, viable, and resource-sparing method to measure partition coefficient P (logP) was developed using reverse-phase high-performance liquid chromatography (RP-HPLC). Highly lipophilic drugs lack reliable, experimentally determined logP values in the literature. The RP-HPLC method reported here can be used for high throughput estimation of logP of commonly used drugs. A larger pool of reliable logP values of commonly used drugs shows promise to improve quality of medicinal chemistry and PK models. Lastly, our goal was to assess, for lipophilic drugs, the impact of logP on human volume distribution at steady state (VDss) predictions using the Oie-Tozer, Rodgers-Rowland, GastroPlus, Korzekwa-Nagar, and TCM-New methods. Sensitivity and prediction error analyses were conducted with a range of logP values and specific logP. TCM-New was shown to be the best method for VDss prediction of highly lipophilic drugs, suggesting blood plasma ratio (BPR) as a favorable surrogate for drug partitioning in the tissues.