Opportunities and Challenges for AI-Based Analysis of RWD in Pharmaceutical R&D: A Practical Perspective

Real world data (RWD) has become an important tool in pharmaceutical research and development. Generated every time patients interact with the healthcare system when diagnoses are developed and medical interventions are selected, RWD are massive and in many regards typical big data. The use of artificial intelligence (AI) to analyze RWD seems an obvious choice. It promises new insights into medical need, drivers of diseases, and new opportunities for pharmacological interventions. When put into practice RWD analyses are challenging. The distributed generation of data, under sub-optimally standardized conditions in a patient-oriented but not information maximizing healthcare transaction, leads to a high level of sparseness and uncontrolled biases. We discuss why this needs to be addressed independent of the type of analysis approach. While classical statistical analysis and modeling approaches provide a rigorous framework for the handling of bias and sparseness, AI methods are not necessarily suited when applied naively. Special precautions need to be taken from choice of method until interpretation of results to prevent potentially harmful fallacies. The conscious use of prior medical subject matter expertise may also be required. Based on typical application examples we illustrate challenges and methodological considerations

Meta-analysis of preclinical measures of efficacy in immune checkpoint blockade therapies and comparison to clinical efficacy estimates

Abstract Background Despite the successes of checkpoint inhibitors targeting T-cell receptors, clinical efficacy is highly cancer-dependent and subject to high inter-individual variability in treatment outcome. The ability to predict the clinical success in different cancer indications is therefore an important capability for successful clinical development. In this meta-analysis, the main goal was to identify factors that modified the clinical efficacy estimates of checkpoint blockade therapies derived from preclinical animal data to improve the robustness and reliability of such estimates. Methods To this end, animal studies testing checkpoint inhibitors (anti-PD-1, anti-PD-L1, anti-CTLA-4) were identified in PubMed ranging from 1.01.2000 to 31.12.2018. The eligibility criteria included the reporting of the Kaplan–Meier estimates of survival and the number of mice used in each experiment. A mixed-effects model was fitted to the preclinical and clinical data separately to determine potential sources of bias and heterogeneity between studies. Results A total of 160 preclinical studies comprising 13,811 mice were selected, from which the hazard ratio (HR) and the median survival ratio (MSR) were calculated. Similarly, clinical Phase III studies of checkpoint inhibitors were identified in PubMed and the ClinicalTrials.gov database ranging from 1.01.2010 to 31.12.2020. This resulted in 62 clinical studies representing 43,135 patients subjected to 8 therapies from which overall survival (OS) and progression-free survival (PFS) hazard ratios were obtained. Using a mixed-effects model, different factors were tested to identify sources of variability between estimates. In the preclinical data, the tumor cell line and individual study were the main factors explaining the heterogeneity. In the clinical setting, the cancer type was influential to the inter-study variability. When using the preclinical estimates to predict clinical estimates, the cancer-type specific estimates of treatment effect using the MSRs better approximated the observed clinical estimates than the HR-derived predictions. Conclusions This has strong implications on the design of ICB preclinical studies with respect to sample size determination, selection of cancer cell lines and labs to run the experiments and the choice of efficacy measure

A generic framework for the physiologically‐based pharmacokinetic platform qualification of PK‐Sim and its application to predicting cytochrome P450 3A4–mediated drug–drug interactions

Abstract The success of applications of physiologically‐based pharmacokinetic (PBPK) modeling in drug development and drug labeling has triggered regulatory agencies to demand rigorous demonstration of the predictive capability of the specific PBPK platform for a particular intended application purpose. The effort needed to comply with such qualification requirements exceeds the costs for any individual PBPK application. Because changes or updates of a PBPK platform would require (re‐)qualification, a reliable and efficient generic qualification framework is needed. We describe the development and implementation of an agile and sustainable technical framework for automatic PBPK platform (re‐)qualification of PK‐Sim® embedded in the open source and open science GitHub landscape of Open Systems Pharmacology. The qualification approach enables the efficient assessment of all aspects relevant to the qualification of a particular purpose and provides transparency and traceability for all stakeholders. As a showcase example for the power and versatility of the qualification framework, we present the qualification of PK‐Sim® for the intended purpose of predicting cytochrome P450 3A4 (CYP3A4)–mediated drug–drug interactions (DDIs). Several perpetrator PBPK models featuring various degrees of CYP3A4 modulation and different types of mechanisms (competitive inhibition, mechanism‐based inactivation, and induction) were coupled with a set of PBPK models of sensitive CYP3A4 victim drugs. Simulations were compared to a comprehensive data set of 135 observations from published clinical DDI studies. The platform's overall predictive performance showed reasonable accuracy and precision (geometric mean fold error of 1.4 for both area under the plasma concentration‐time curve ratios and peak plasma concentration ratios with/without perpetrator) and suggests that PK‐Sim® can be applied to quantitatively assess CYP3A4‐mediated DDI in clinically untested scenarios

A generic framework for the physiologically‐based pharmacokinetic platform qualification of PK‐Sim and its application to predicting cytochrome P450 3A4–mediated drug–drug interactions

Abstract The success of applications of physiologically‐based pharmacokinetic (PBPK) modeling in drug development and drug labeling has triggered regulatory agencies to demand rigorous demonstration of the predictive capability of the specific PBPK platform for a particular intended application purpose. The effort needed to comply with such qualification requirements exceeds the costs for any individual PBPK application. Because changes or updates of a PBPK platform would require (re‐)qualification, a reliable and efficient generic qualification framework is needed. We describe the development and implementation of an agile and sustainable technical framework for automatic PBPK platform (re‐)qualification of PK‐Sim® embedded in the open source and open science GitHub landscape of Open Systems Pharmacology. The qualification approach enables the efficient assessment of all aspects relevant to the qualification of a particular purpose and provides transparency and traceability for all stakeholders. As a showcase example for the power and versatility of the qualification framework, we present the qualification of PK‐Sim® for the intended purpose of predicting cytochrome P450 3A4 (CYP3A4)–mediated drug–drug interactions (DDIs). Several perpetrator PBPK models featuring various degrees of CYP3A4 modulation and different types of mechanisms (competitive inhibition, mechanism‐based inactivation, and induction) were coupled with a set of PBPK models of sensitive CYP3A4 victim drugs. Simulations were compared to a comprehensive data set of 135 observations from published clinical DDI studies. The platform's overall predictive performance showed reasonable accuracy and precision (geometric mean fold error of 1.4 for both area under the plasma concentration‐time curve ratios and peak plasma concentration ratios with/without perpetrator) and suggests that PK‐Sim® can be applied to quantitatively assess CYP3A4‐mediated DDI in clinically untested scenarios

Evaluation of the Efficacy and Safety of Rivaroxaban Using a Computer Model for Blood Coagulation

Rivaroxaban is an oral, direct Factor Xa inhibitor approved in the European Union and several other countries for the prevention of venous thromboembolism in adult patients undergoing elective hip or knee replacement surgery and is in advanced clinical development for the treatment of thromboembolic disorders. Its mechanism of action is antithrombin independent and differs from that of other anticoagulants, such as warfarin (a vitamin K antagonist), enoxaparin (an indirect thrombin/Factor Xa inhibitor) and dabigatran (a direct thrombin inhibitor). A blood coagulation computer model has been developed, based on several published models and preclinical and clinical data. Unlike previous models, the current model takes into account both the intrinsic and extrinsic pathways of the coagulation cascade, and possesses some unique features, including a blood flow component and a portfolio of drug action mechanisms. This study aimed to use the model to compare the mechanism of action of rivaroxaban with that of warfarin, and to evaluate the efficacy and safety of different rivaroxaban doses with other anticoagulants included in the model. Rather than reproducing known standard clinical measurements, such as the prothrombin time and activated partial thromboplastin time clotting tests, the anticoagulant benchmarking was based on a simulation of physiologically plausible clotting scenarios. Compared with warfarin, rivaroxaban showed a favourable sensitivity for tissue factor concentration inducing clotting, and a steep concentration-effect relationship, rapidly flattening towards higher inhibitor concentrations, both suggesting a broad therapeutic window. The predicted dosing window is highly accordant with the final dose recommendation based upon extensive clinical studies

Pharmacokinetics of rivaroxaban in children using physiologically based and population pharmacokinetic modelling: an EINSTEIN-Jr phase I study

Abstract Background The EINSTEIN-Jr program will evaluate rivaroxaban for the treatment of venous thromboembolism (VTE) in children, targeting exposures similar to the 20 mg once-daily dose for adults. A physiologically based pharmacokinetic (PBPK) model for pediatric rivaroxaban dosing has been constructed. Methods We quantitatively assessed the pharmacokinetics (PK) of a single rivaroxaban dose in children using population pharmacokinetic (PopPK) modelling and assessed the applicability of the PBPK model. Plasma concentration–time data from the EINSTEIN-Jr phase I study were analysed by non-compartmental and PopPK analyses and compared with the predictions of the PBPK model. Two rivaroxaban dose levels, equivalent to adult doses of rivaroxaban 10 mg and 20 mg, and two different formulations (tablet and oral suspension) were tested in children aged 0.5–18 years who had completed treatment for VTE. Results PK data from 59 children were obtained. The observed plasma concentration–time profiles in all subjects were mostly within the 90% prediction interval, irrespective of dose or formulation. The PopPK estimates and non-compartmental analysis-derived PK parameters (in children aged ≥6 years) were in good agreement with the PBPK model predictions. Conclusions These results confirmed the applicability of the rivaroxaban pediatric PBPK model in the pediatric population aged 0.5–18 years, which in combination with the PopPK model, will be further used to guide dose selection for the treatment of VTE with rivaroxaban in EINSTEIN-Jr phase II and III studies. Trial registration ClinicalTrials.gov number, NCT01145859; registration date: 17 June 2010

Baseline clusters and the response to positive airway pressure treatment in obstructive sleep apnoea patients: longitudinal data from the European Sleep Apnea Database cohort

International audienceIntroduction The European Sleep Apnea Database was used to identify distinguishable obstructive sleep apnoea (OSA) phenotypes and to investigate the clinical outcome during positive airway pressure (PAP) treatment. Method Prospective OSA patient data were recruited from 35 sleep clinics in 21 European countries. Unsupervised cluster analysis (anthropometrics, clinical variables) was performed in a random sample (n=5000). Subsequently, all patients were assigned to the clusters using a conditional inference tree classifier. Responses to PAP treatment change in apnoea severity and Epworth sleepiness scale (ESS) were assessed in relation to baseline patient clusters and at short- and long-term follow-up. Results At baseline, 20 164 patients were assigned (mean age 54.1±12.2 years, 73% male, median apnoea–hypopnoea index (AHI) 27.3 (interquartile range (IQR) 14.1–49.3) events·h −1 , and ESS 9.8±5.3) to seven distinct clusters based on anthropometrics, comorbidities and symptoms. At PAP follow-up (median 210 [IQR 134–465] days), the observed AHI reduction (n=1075) was similar, whereas the ESS response (n=3938) varied: largest reduction in cluster 3 (young healthy symptomatic males) and 6 (symptomatic males with psychiatric disorders, −5.0 and −5.1 units, respectively (all p<0.01), limited reduction in clusters 2 (obese males with systemic hypertension) and 5 (elderly multimorbid obese males, −4.2 (p<0.05) and −3.7 (p<0.001), respectively). Residual sleepiness in cluster 5 was particularly evident at long-term follow-up (p<0.05). Conclusion OSA patients can be classified into clusters based on clinically identifiable features. Importantly, these clusters may be useful for prediction of both short- and long-term responses to PAP intervention