Clinical Decision Support for Precision Dosing: Opportunities for Enhanced Equity and Inclusion in Health Care.

Precision dosing aims to tailor doses to individual patients with the goal of improving treatment efficacy and avoiding toxicity. Clinical decision support software (CDSS) plays a crucial role in mediating this process, translating knowledge derived from clinical trials and real-world data (RWD) into actionable insights for clinicians to use at the point of care. However, not all patient populations are proportionally represented in clinical trials and other data sources that inform CDSS tools, limiting the applicability of these tools for underrepresented populations. Here, we review some of the limitations of existing CDSS tools and discuss methods for overcoming these gaps. We discuss considerations for study design and modeling to create more inclusive CDSS, particularly with an eye toward better incorporation of biological indicators in place of race, ethnicity, or sex. We also review inclusive practices for collection of these demographic data, during both study design and in software user interface design. Because of the role CDSS plays in both recording routine clinical care data and disseminating knowledge derived from data, CDSS presents a promising opportunity to continuously improve precision dosing algorithms using RWD to better reflect the diversity of patient populations.

Assessment of a Model-Informed Precision Dosing Platform Use in Routine Clinical Care for Personalized Busulfan Therapy in the Pediatric Hematopoietic Cell Transplantation (HCT) Population.

INTRODUCTION: Population pharmacokinetic (PK) studies demonstrate model-based dosing for busulfan that incorporates body size and age improve clinical target attainment as compared to weight-based regimens. Recently, for clinical dosing of busulfan and TDM, our institution transitioned to a cloud-based clinical decision support tool (www.insight-rx.com). The goal of this study was to assess the dose decision tool for the achievement of target exposure of busulfan in children undergoing hematopoietic cell transplantation (HCT). PATIENTS AND METHODS: Patients (N = 188) were grouped into cohorts A, B, or C based on the method for initial dose calculation and estimation of AUC: Cohort A: Initial doses were based on the conventional dosing algorithm (as outlined in the manufacturers' package insert) and non-compartmental analysis (NCA) estimation using the trapezoidal rule for estimation of AUC following TDM. Cohort B: Initial doses for busulfan were estimated by a first-generation PK model and NCA estimation of AUC following TDM. Cohort C: Initial doses were calculated by an updated, second-generation PK model available in the dose decision tool with an estimation of AUC following TDM. RESULTS: The percent of individuals achieving the exposure target at the time of first PK collection was higher in subjects receiving initial doses provided by the model-informed precision dosing platform (cohort C, 75%) versus subjects receiving initial doses based on either of the two other approaches (conventional guidelines/cohort A, 25%; previous population PK model and NCA parameter estimation, cohort B, 50%). Similarly, the percent of subjects achieving the targeted cumulative busulfan exposure (cAUC) in cohort C was 100% vs. 66% and 88% for cohort A and B, respectively. For cAUC, the variability in the spread of target attainment (%CV) was low at 4.1% for cohort C as compared to cohort A (14.8%) and cohort B (17.1%). CONCLUSION: Achievement of goal exposure early on in treatment was improved with the updated model for busulfan and the Bayesian platform. Model-informed dosing and TDM utilizing a Bayesian-based platform provides a significant advantage over conventional guidelines for the achievement of goal cAUC exposure.

Model-based treatment optimization of a novel VEGFR inhibitor.

AIM: To evaluate dosing and intervention strategies for the phase II programme of a VEGF receptor inhibitor using PK-PD modelling and simulation, with the aim of maximizing (i) the number of patients on treatment and (ii) the average dose level during treatment. METHODS: A previously developed PK-PD model for lenvatinib (E7080) was updated and parameters were re-estimated (141 patients, once daily and twice daily regimens). Treatment of lenvatinib was simulated for 16 weeks, initiated at 25 mg once daily. Outcome measures included the number of patients on treatment and overall drug exposure. A hypertension intervention design proposed for phase II studies was evaluated, including antihypertensive treatment and dose de-escalation. Additionally, a within-patient dose escalation was investigated, titrating up to 50 mg once daily unless unacceptable toxicity occurred. RESULTS: Using the proposed antihypertension intervention design, 82% of patients could remain on treatment, and the mean dose administered was 21.5 mg day⁻¹. The adverse event (AE) guided dose titration increased the average dose by 4.6 mg day⁻¹, while only marginally increasing the percentage of patients dropping out due to toxicity (from 18% to 20.8%). CONCLUSIONS: The proposed hypertension intervention design is expected to be effective in maintaining patients on treatment with lenvatinib. The AE-guided dose titration with blood pressure as a biomarker yielded a higher overall dose level, without relevant increases in toxicity. Since increased exposure to lenvatinib seems correlated with increased treatment efficacy, the adaptive treatment design may thus be a valid approach to improve treatment outcome.