Contribution of machine learning to tumor growth inhibition modeling for hepatocellular carcinoma patients under Roblitinib (FGF401) drug treatment

Clinical decision support for chemotherapy-induced neutropenia using a hybrid pharmacodynamic/machine learning model

Consensus guidelines recommend use of granulocyte colony stimulating factor in patients deemed at risk of chemotherapy-induced neutropenia, however, these risk models are limited in the factors they consider and miss some cases of neutropenia. Clinical decision making could be supported using models that better tailor their predictions to the individual patient using the wealth of data available in electronic health records (EHRs). Here, we present a hybrid pharmacokinetic/pharmacodynamic (PKPD)/machine learning (ML) approach that uses predictions and individual Bayesian parameter estimates from a PKPD model to enrich an ML model built on her data. We demonstrate this approach using models developed on a large real-world data set of 9121 patients treated for lymphoma, breast, or thoracic cancer. We also investigate the benefits of augmenting the training data using synthetic data simulated with the PKPD model. We find that PKPD-enrichment of ML models improves prediction of grade 3-4 neutropenia, as measured by higher precision (61%) and recall (39%) compared to PKPD model predictions (47%, 33%) or base ML model predictions (51%, 31%). PKPD augmentation of ML models showed minor improvements in recall (44%) but not precision (56%), and data augmentation required careful tuning to control overfitting its predictions to the PKPD model. PKPD enrichment of ML shows promise for leveraging both the physiology-informed predictions of PKPD and the ability of ML to learn predictor-outcome relationships from large data sets to predict patient response to drugs in a clinical precision dosing context.

Integration of Pharmacokinetics, Pharmacodynamics, Safety, and Efficacy into Model-Informed Dose Selection in Oncology First-in-Human Study: A Case of Roblitinib (FGF401)

Model-informed dose selection has been drawing increasing interest in oncology early clinical development. The current paper describes the example of FGF401, a selective fibroblast growth factor receptor 4 (FGFR4) inhibitor, in which a comprehensive modeling and simulation (M&S) framework, using both pharmacometrics and statistical methods, was established during its first-in-human clinical development using the totality of pharmacokinetics (PK), pharmacodynamic (PD) biomarkers, and safety and efficacy data in patients with cancer. These M&S results were used to inform FGF401 dose selection for future development. A two-compartment population PK (PopPK) model with a delayed 0-order absorption and linear elimination adequately described FGF401 PK. Indirect PopPK/PD models including a precursor compartment were independently established for two biomarkers: circulating FGF19 and 7α-hydroxy-4-cholesten-3-one (C4). Model simulations indicated a close-to-maximal PD effect achieved at the clinical exposure range. Time-to-progression was analyzed by Kaplan-Meier method which favored a trough concentration (C_trough )-driven efficacy requiring C_trough above a threshold close to the drug concentration producing 90% inhibition of phospho-FGFR4. Clinical tumor growth inhibition was described by a PopPK/PD model that reproduced the dose-dependent effect on tumor growth. Exposure-safety analyses on the expected on-target adverse events, including elevation of aspartate aminotransferase and diarrhea, indicated a lack of clinically relevant relationship with FGF401 exposure. Simulations from an indirect PopPK/PD model established for alanine aminotransferase, including a chain of three precursor compartments, further supported that maximal target inhibition was achieved and there was a lack of safety-exposure relationship. This M&S framework supported a dose selection of 120 mg once daily fasted or with a low-fat meal and provides a practical example that might be applied broadly in oncology early clinical development.

A Systems-Based Analysis of Mono- and Combination Therapy for Carbapenem-Resistant Klebsiella pneumoniae Bloodstream Infections

Antimicrobial resistance is a global threat. As "proof-of-concept," we employed a system-based approach to identify patient, bacterial, and drug variables contributing to mortality in patients with carbapenem-resistant Klebsiella pneumoniae (CRKp) bloodstream infections exposed to colistin (COL) and ceftazidime-avibactam (CAZ/AVI) as mono- or combination therapies. Patients (n = 49) and CRKp isolates (n = 22) were part of the Consortium on Resistance Against Carbapenems in Klebsiella and other Enterobacteriaceae (CRACKLE-1), a multicenter, observational, prospective study of patients with carbapenem-resistant Enterobacterales (CRE) conducted between 2011 and 2016. Pharmacodynamic activity of mono- and combination drug concentrations was evaluated over 24 h using in vitro static time-kill assays. Bacterial growth and killing dynamics were estimated with a mechanism-based model. Random Forest was used to rank variables important for predicting 30-day mortality. Isolates exposed to COL+CAZ/AVI had enhanced early bacterial killing compared to CAZ/AVI alone and fewer incidences of regrowth compared to COL and CAZ/AVI. The mean coefficient of determination (R2) for the observed versus predicted bacterial counts was 0.86 (range: 0.75 - 0.95). Bacterial subpopulation susceptibilities and drug mechanistic synergy were essential to describe bacterial killing and growth dynamics. The combination of clinical (hypotension), bacterial (IncR plasmid, aadA2, and sul3) and drug (KC50) variables were most predictive of 30-day mortality. This proof-of-concept study combined clinical, bacterial, and drug variables in a unified model to evaluate clinical outcomes.

Contribution of machine learning to tumor growth inhibition modeling for hepatocellular carcinoma patients under Roblitinib (FGF401) drug treatment

Machine learning (ML) opens new perspectives in identifying predictive factors of efficacy among a large number of patients’ characteristics in oncology studies. The objective of this work was to combine ML with population pharmacokinetic/pharmacodynamic (PK/PD) modeling of tumor growth inhibition to understand the sources of variability between patients and therefore improve model predictions to support drug development decisions. Data from 127 patients with hepatocellular carcinoma enrolled in a phase I/II study evaluating once‐daily oral doses of the fibroblast growth factor receptor FGFR4 kinase inhibitor, Roblitinib (FGF401), were used. Roblitinib  PKs was best described by a two‐compartment model with a delayed zero‐order absorption and linear elimination. Clinical efficacy using the longitudinal sum of the longest lesion diameter data was described with a population PK/PD model of tumor growth inhibition including resistance to treatment. ML, applying elastic net modeling of time to progression data, was associated with cross‐validation, and allowed to derive a composite predictive risk score from a set of 75 patients’ baseline characteristics. The two approaches were combined by testing the inclusion of the continuous risk score as a covariate on PD model parameters. The score was found as a significant covariate on the resistance parameter and resulted in 19% reduction of its variability, and 32% variability reduction on the average dose for stasis. The final PK/PD model was used to simulate effect of patients’ characteristics on tumor growth inhibition profiles. The proposed methodology can be used to support drug development decisions, especially when large interpatient variability is observed.