Integrated Simulation Framework for Toxicity, Dose Intensity, Disease Progression, and Cost Effectiveness for Castration-Resistant Prostate Cancer Treatment With Eribulin

The role of economic evaluations in advancing HIV multipurpose prevention technologies in early-stage development

Product development is a high-risk undertaking, especially so when investments are prioritized for low- and middle-income countries (LMICs) where markets may be smaller, fragile, and resource-constrained. New HIV prevention technologies, such as the dapivirine vaginal ring (DVR) and long-acting injectable cabotegravir (CAB-LA), are being introduced to these markets with one indication, meeting different needs of groups such as adolescent girls and young women (AGYW) and female sex workers (FSWs) in settings with high HIV burden. However, limited supply and demand have made their uptake a challenge. Economic evaluations conducted before Phase III trials can help optimize the potential public health value proposition of products in early-stage research and development (R&D), targeting investments in the development pathway that result in products likely to be available and taken up. Public investors in the HIV prevention pipeline, in particular those focused on innovative presentations such as multipurpose prevention technologies (MPTs), can leverage early economic evaluations to understand the intrinsic uncertainty in market characterization. In this perspective piece, we reflect on the role of economic evaluations in early product development and on methodological considerations that are central to these analyses. We also discuss methods, in quantitative and qualitative research that can be deployed in early economic evaluations to address uncertainty, with examples applied to the development of future technologies for HIV prevention and MPTs.

Assessment of ibrutinib scheduling on leukocyte, lymph node size and blood pressure dynamics in chronic lymphocytic leukemia through pharmacokinetic-pharmacodynamic models

Ibrutinib is a Bruton tyrosine kinase (Btk) inhibitor for treating chronic lymphocytic leukemia (CLL). It has also been associated with hypertension. The optimal dosing schedule for mitigating this adverse effect is currently under discussion. A quantification of relationships between systemic ibrutinib exposure and efficacy (i.e., leukocyte count and sum of the product of perpendicular diameters [SPD] of lymph nodes) and hypertension toxicity (i.e., blood pressure), and their association with overall survival is needed. Here, we present a semi-mechanistic pharmacokinetic-pharmacodynamic modeling framework to characterize such relationships and facilitate dose optimization. Data from a phase Ib/II study were used, including ibrutinib plasma concentrations to derive daily 0-24-h area under the concentration-time curve, leukocyte count, SPD, survival, and blood pressure measurements. A nonlinear mixed effects modeling approach was applied, considering ibrutinib's pharmacological action and CLL cell dynamics. The final framework included (i) an integrated model for SPD and leukocytes consisting of four CLL cell subpopulations with ibrutinib inhibiting phosphorylated Btk production, (ii) a turnover model in which ibrutinib stimulates an increase in blood pressure, and (iii) a competing risk model for dropout and death. Simulations predicted that the approved dosing schedule had a slightly higher efficacy (24-month, progression-free survival [PFS] 98%) than de-escalation schedules (24-month, average PFS ≈ 97%); the latter had, on average, ≈20% lower proportions of patients with hypertension. The developed modeling framework offers an improved understanding of the relationships among ibrutinib exposure, efficacy and toxicity biomarkers. This framework can serve as a platform to assess dosing schedules in a biologically plausible manner.

Opportunities and Challenges of Disease Progression Modeling in Drug Development - An IQ Perspective

Disease progression modeling (DPM) represents an important model-informed drug development framework. The scientific communities support the use of DPM to accelerate and increase efficiency in drug development. This article summarizes International Consortium for Innovation & Quality (IQ) in Pharmaceutical Development mediated survey conducted across multiple biopharmaceutical companies on challenges and opportunities for DPM. Additionally, this summary highlights the viewpoints of IQ from the 2021 workshop hosted by the US Food and Drug Administration (FDA). Sixteen pharmaceutical companies participated in the IQ survey with 36 main questions. The types of questions included single/multiple choice, dichotomous, rank questions, and open-ended or free text. The key results show that DPM has different representation, it encompasses natural disease history, placebo response, standard of care as background therapy, and can even be interpreted as pharmacokinetic/pharmacodynamic modeling. The most common reasons for not implementing DPM as frequently seem to be difficulties in internal cross-functional alignment, lack of knowledge of disease/data, and time constraints. If successfully implemented, DPM can have an impact on dose selection, reduction of sample size, trial read-out support, patient selection/stratification, and supportive evidence for regulatory interactions. The key success factors and key challenges of disease progression models were highlighted in the survey and about 24 case studies across different therapeutic areas were submitted from various survey sponsors. Although DPM is still evolving, its current impact is limited but promising. The success of such models in the future will depend on collaboration, advanced analytics, availability of and access to relevant and adequate-quality data, collaborative regulatory guidance, and published examples of impact.

Identification of high-dimensional omics-derived predictors for tumor growth dynamics using machine learning and pharmacometric modeling

Pharmacometric modeling can capture tumor growth inhibition (TGI) dynamics and variability. These approaches do not usually consider covariates in high-dimensional settings, whereas high-dimensional molecular profiling technologies ("omics") are being increasingly considered for prediction of anticancer drug treatment response. Machine learning (ML) approaches have been applied to identify high-dimensional omics predictors for treatment outcome. Here, we aimed to combine TGI modeling and ML approaches for two distinct aims: omics-based prediction of tumor growth profiles and identification of pathways associated with treatment response and resistance. We propose a two-step approach combining ML using least absolute shrinkage and selection operator (LASSO) regression with pharmacometric modeling. We demonstrate our workflow using a previously published dataset consisting of 4706 tumor growth profiles of patient-derived xenograft (PDX) models treated with a variety of mono- and combination regimens. Pharmacometric TGI models were fit to the tumor growth profiles. The obtained empirical Bayes estimates-derived TGI parameter values were regressed using the LASSO on high-dimensional genomic copy number variation data, which contained over 20,000 variables. The predictive model was able to decrease median prediction error by 4% as compared with a model without any genomic information. A total of 74 pathways were identified as related to treatment response or resistance development by LASSO, of which part was verified by literature. In conclusion, we demonstrate how the combined use of ML and pharmacometric modeling can be used to gain pharmacological understanding in genomic factors driving variation in treatment response.

Linked Pharmacometric-Pharmacoeconomic Modeling and Simulation in Clinical Drug Development

Market access and pricing of pharmaceuticals are increasingly contingent on the ability to demonstrate comparative effectiveness and cost-effectiveness. As such, it is widely recognized that predictions of the economic potential of drug candidates in development could inform decisions across the product life cycle. This may be challenging when safety and efficacy profiles in terms of the relevant clinical outcomes are unknown or highly uncertain early in product development. Linking pharmacometrics and pharmacoeconomics, such that outputs from pharmacometric models serve as inputs to pharmacoeconomic models, may provide a framework for extrapolating from early-phase studies to predict economic outcomes and characterize decision uncertainty. This article reviews the published studies that have implemented this methodology and used simulation to inform drug development decisions and/or to optimize the use of drug treatments. Some of the key practical issues involved in linking pharmacometrics and pharmacoeconomics, including the choice of final outcome measures, methods of incorporating evidence on comparator treatments, approaches to handling multiple intermediate end points, approaches to quantifying uncertainty, and issues of model validation are also discussed. Finally, we have considered the potential barriers that may have limited the adoption of this methodology and suggest that closer alignment between the disciplines of clinical pharmacology, pharmacometrics, and pharmacoeconomics, may help to realize the potential benefits associated with linked pharmacometric-pharmacoeconomic modeling and simulation.

Incorporating Pharmacometrics into Pharmacoeconomic Models: Applications from Drug Development

Pharmacometrics is the science of quantifying the relationship between the pharmacokinetics and pharmacodynamics of drugs in combination with disease models and trial information to aid in drug development and dosing optimization for clinical practice. Considering the variability in the dose-concentration-effect relationship of drugs, an opportunity exists in linking pharmacokinetic and pharmacodynamic model-based estimates with pharmacoeconomic models. This link may provide early estimates of the cost effectiveness of drug therapies, thus informing late-stage drug development, pricing, and reimbursement decisions. Published case studies have demonstrated how integrated pharmacokinetic-pharmacodynamic-pharmacoeconomic models can complement traditional pharmacoeconomic analyses by identifying the impact of specific patient sub-groups, dose, dosing schedules, and adherence on the cost effectiveness of drugs, thus providing a mechanistic basis to predict the economic value of new drugs. Greater collaboration between the pharmacoeconomics and pharmacometrics community can enable methodological improvements in pharmacokinetic-pharmacodynamic-pharmacoeconomic models to support drug development.

Clinical Pharmacokinetics and Pharmacodynamics of Immune Checkpoint Inhibitors

Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical impact in improving overall survival of several malignancies associated with poor outcomes; however, only 20–40% of patients will show long-lasting survival. Further clarification of factors related to treatment response can support improvements in clinical outcome and guide the development of novel immune checkpoint therapies. In this article, we have provided an overview of the pharmacokinetic (PK) aspects related to current ICIs, which include target-mediated drug disposition and time-varying drug clearance. In response to the variation in treatment exposure of ICIs and the significant healthcare costs associated with these agents, arguments for both dose individualization and generalization are provided. We address important issues related to the efficacy and safety, the pharmacodynamics (PD), of ICIs, including exposure–response relationships related to clinical outcome. The unique PK and PD aspects of ICIs give rise to issues of confounding and suboptimal surrogate endpoints that complicate interpretation of exposure–response analysis. Biomarkers to identify patients benefiting from treatment with ICIs have been brought forward. However, validated biomarkers to monitor treatment response are currently lacking.

Precision Dosing Priority Criteria: Drug, Disease, and Patient Population Variables

The administered dose of a drug modulates whether patients will experience optimal effectiveness, toxicity including death, or no effect at all. Dosing is particularly important for diseases and/or drugs where the drug can decrease severe morbidity or prolong life. Likewise, dosing is important where the drug can cause death or severe morbidity. Since we believe there are many examples where more precise dosing could benefit patients, it is worthwhile to consider how to prioritize drug-disease targets. One key consideration is the quality of information available from which more precise dosing recommendations can be constructed. When a new more precise dosing scheme is created and differs significantly from the approved label, it is important to consider the level of proof necessary to either change the label and/or change clinical practice. The cost and effort needed to provide this proof should also be considered in prioritizing drug-disease precision dosing targets. Although precision dosing is being promoted and has great promise, it is underutilized in many drugs and disease states. Therefore, we believe it is important to consider how more precise dosing is going to be delivered to high priority patients in a timely manner. If better dosing schemes do not change clinical practice resulting in better patient outcomes, then what is the use? This review paper discusses variables to consider when prioritizing precision dosing candidates while highlighting key examples of precision dosing that have been successfully used to improve patient care.

Model-Based Biomarker Selection for Dose Individualization of Tyrosine-Kinase Inhibitors

Tyrosine-kinase inhibitors (TKIs) demonstrate high inter-individual variability with respect to safety and efficacy and would therefore benefit from dose or schedule adjustments. This study investigated the efficacy, safety, and economical aspects of alternative dosing options for sunitinib in gastro-intestinal stromal tumors (GIST) and axitinib in metastatic renal cell carcinoma (mRCC). Dose individualization based on drug concentration, adverse effects, and sVEGFR-3 was explored using a modeling framework connecting pharmacokinetic and pharmacodynamic models, as well as overall survival. Model-based simulations were performed to investigate four different scenarios: (I) the predicted value of high-dose pulsatile schedules to improve clinical outcomes as compared to regular daily dosing, (II) the potential of biomarkers for dose individualizations, such as drug concentrations, toxicity measurements, and the biomarker sVEGFR-3, (III) the cost-effectiveness of biomarker-guided dose-individualizations, and (IV) model-based dosing approaches versus standard sample-based methods to guide dose adjustments in clinical practice. Simulations from the axitinib and sunitinib frameworks suggest that weekly or once every two weeks high-dosing result in lower overall survival in patients with mRCC and GIST, compared to continuous daily dosing. Moreover, sVEGFR-3 appears a safe and cost-effective biomarker to guide dose adjustments and improve overall survival (€36 784.- per QALY). Model-based estimations were for biomarkers in general found to correctly predict dose adjustments similar to or more accurately than single clinical measurements and might therefore guide dose adjustments. A simulation framework represents a rapid and resource saving method to explore various propositions for dose and schedule adjustments of TKIs, while accounting for complicating factors such as circulating biomarker dynamics and inter-or intra-individual variability.

Cost-effectiveness analyses and cost analyses in castration-resistant prostate cancer: A systematic review

Background

Treatment of metastatic prostate cancer is associated with high personal and economic burden. Recently, new treatment options for castration-resistant prostate cancer became available with promising survival advantages. However, cost-effectiveness of those new treatment options is sometimes ambiguous or given only under certain circumstances. The aim of this study was to systematically review studies on the cost-effectiveness of treatments and costs of castration-resistant prostate cancer (CRPC) and metastasizing castration-resistant prostate cancer (mCRPC) on their methodological quality and the risk of bias.

Methods

A systematic literature search was performed in the databases PubMed, CINAHL Complete, the Cochrane Library and Web of Science Core Collection for costs-effectiveness analyses, model-based economic evaluations, cost-of-illness analyses and budget impact analyses. Reported costs were inflated to 2015 US$ purchasing power parities. Quality assessment and risk of bias assessment was performed using the Consolidated Health Economic Evaluation Reporting Standards checklist and the Bias in Economic Evaluations checklist, respectively.

Results

In total, 38 articles were identified by the systematic literature search. The methodological quality of the included studies varied widely, and there was considerable risk of bias. The cost-effectiveness treatments for CRPC and mCRPC was assessed with incremental cost-effectiveness ratios ranging from dominance for mitoxantrone to $562,328 per quality-adjusted life year gained for sipuleucel-T compared with prednisone alone. Annual costs for the treatment of castration-resistant prostate cancer ranged from $3,067 to $77,725.

Conclusion

The cost-effectiveness of treatments of CRPC strongly depended on the willingness to pay per quality-adjusted life year gained/life-year saved throughout all included costs-effectiveness analyses and model-based economic evaluations. High-quality cost-effectiveness analyses based on randomized controlled trials are needed in order to make informed decisions on the management of castration-resistant prostate cancer and the resulting financial impact on the healthcare system.

Impact of Non-Adherence and Flare Resolution on the Cost-Effectiveness of Treatments for Gout: Application of a Linked Pharmacometric/Pharmacoeconomic Model

BACKGROUND

Dual urate-lowering therapy (ULT) with lesinurad in combination with either allopurinol or febuxostat is an option for patients with gout unsuccessfully treated on either monotherapy. Treatment failure is often a result of poor medication adherence. Imperfect adherence in clinical trials may lead to biased estimates of treatment effect and confound the results of cost-effectiveness analyses.

OBJECTIVES

To estimate the impact of varying medication adherence on the cost effectiveness of lesinurad dual therapy and estimate the value-based price of lesinurad at which the incremental cost-effectiveness ratio is equal to £20,000 per quality-adjusted life-year (QALY).

METHODS

Treatment effect was simulated using published pharmacokinetic-pharmacodynamic models and scenarios representing adherence in clinical trials, routine practice, and perfect use. The subsequent cost and health impacts, over the lifetime of a patient cohort, were estimated using a bespoke pharmacoeconomic model.

RESULTS

The base-case incremental cost-effectiveness ratios comparing lesinurad dual ULT with monotherapy ranged from £39,184 to £78,350/QALY gained using allopurinol and £31,901 to £124,212/QALY gained using febuxostat, depending on the assumed medication adherence. Results assuming perfect medication adherence imply a per-quarter value-based price of lesinurad of £45.14 when used in dual ULT compared with allopurinol alone and £57.75 compared with febuxostat alone, falling to £25.41 and £3.49, respectively, in simulations of worsening medication adherence.

CONCLUSIONS

The estimated value-based prices of lesinurad only exceeded that which has been proposed in the United Kingdom when assuming both perfect drug adherence and the eradication of gout flares in sustained treatment responders.

Systems Pharmacology: Defining the Interactions of Drug Combinations

The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.

A PK/PD Analysis of Circulating Biomarkers and Their Relationship to Tumor Response in Atezolizumab-Treated non-small Cell Lung Cancer Patients

To assess circulating biomarkers as predictors of antitumor response to atezolizumab (anti-programmed death-ligand 1 (PD-L1), Tecentriq) serum pharmacokinetic (PK) and 95 plasma biomarkers were analyzed in 88 patients with relapsed/refractory non-small cell lung cancer (NSCLC) receiving atezolizumab i.v. q3w (10-20 mg/kg) in the PCD4989g phase I clinical trial. Following exploratory analyses, two plasma biomarkers were chosen for further study and correlation with change in tumor size (the sum of the longest diameter) was assessed in a pharmacokinetic/pharmacodynamic (PK/PD) tumor modeling framework. When longitudinal kinetics of biomarkers and tumor size were modeled, tumor shrinkage was found to significantly correlate with area under the curve (AUC), baseline factors (metastatic sites, liver metastases, and smoking status), and relative change in interleukin (IL)-18 level from baseline at day 21 (RCFB_{IL -18,d21} ). Although AUC was a major predictor of tumor shrinkage, the effect was estimated to dissipate with an average half-life of 80 days, whereas RCFB_{IL -18,d21} seemed relevant to the duration of the response.

Innovation at the Intersection of Clinical Trials and Real-World Data Science to Advance Patient Care

While efficacy and safety data collected from randomized clinical trials are the evidentiary standard for determining market authorization, this alone may no longer be sufficient to address the needs of key stakeholders (regulators, providers, and payers) and guarantee long-term success of pharmaceutical products. There is a heightened interest from stakeholders on understanding the use of real-world evidence (RWE) to substantiate benefit-risk assessment and support the value of a new drug. This review provides an overview of real-world data (RWD) and related advances in the regulatory framework, and discusses their impact on clinical research and development. A framework for linking drug development decisions with the value proposition of the drug, utilizing pharmacokinetic-pharmacodynamic-pharmacoeconomic models, is introduced. The summary presented here is based on the presentations and discussion at the symposium entitled Innovation at the Intersection of Clinical Trials and Real-World Data to Advance Patient Care at the American Society for Clinical Pharmacology and Therapeutics (ASCPT) 2017 Annual Meeting.

Translating Pharmacometrics to a Pharmacoeconomic Model of COPD

BACKGROUND

A model-based meta-analysis (MBMA) is a type of meta-regression that uses nonlinear mixed-effects models estimated on trial-level data to relate patient and trial characteristics, dosing, biomarkers, and outcomes of treatment.

OBJECTIVES

To use a pharmacometric MBMA within a pharmacoeconomic model of chronic obstructive pulmonary disease (COPD).

METHODS

A Markov microsimulation model was developed to estimate monthly changes in the key disease severity metrics of COPD (forced expiratory volume in 1 second [FEV₁] and exacerbations) to compare a hypothetical drug that increases FEV₁ to usual care. The MBMA was used to predict a baseline exacerbation rate in a group of actual trial patients, given their known baseline FEV₁. The hypothetical drug increased FEV₁, thereby decreasing individuals' predicted exacerbation rates. Individual patient simulations allowed stochastic changes in monthly FEV₁ decline.

RESULTS

In a sample of 1097 trial patients with a mean FEV₁ of 50%, the MBMA predicted 0.93 exacerbations per year on average. The exacerbation rate ranged from 0.52 to 1.3 per year across moderate and severe patient subgroups. A hypothetical anti-inflammatory drug that increased FEV₁ by 50 ml decreased exacerbations by 26%. Given a simplified estimation of costs and quality-adjusted life-years (QALYs) associated with COPD, a drug with a 50-ml increase priced at €35/mo had an incremental cost-effectiveness ratio ranging from €13,000/QALY to approximately €207,000/QALY across patient severity subgroups.

CONCLUSIONS

The synergistic aspects of MBMA and pharmacoeconomic modeling are highlighted in this hypothetical example. Markov microsimulation modeling allows the finer predictions of MBMA to inform parameters. Such an approach has utility in both early-phase cost-effectiveness estimations and trial design.

Disease Progression Modeling: Key Concepts and Recent Developments

Disease modeling involves the use of mathematical functions to describe quantitatively the time course of disease progression. In order to characterize the natural progression of disease, these models generally incorporate longitudinal data for some biomarker(s) of disease severity or can incorporate more direct measures of disease severity. Disease models are also often linked to pharmacokinetic-pharmacodynamic models so that the influence of drug treatment on disease progression can be quantified and evaluated. Regulatory agencies have embraced disease progression models as powerful tools that can be used to improve drug development productivity. This article provides a brief overview of key concepts in disease progression modeling followed by illustrative examples from models for Alzheimer's disease. Finally, recent novel applications in which disease progression models have been linked to cost-effectiveness analysis and genomic analysis are described.