Exposure-response modeling of clinical end points using latent variable indirect response models

Visual predictive check of longitudinal models and dropout

Visual predictive checks (VPC) are commonly used to evaluate pharmacometrics models. However their performance may be hampered if patients with worse outcomes drop out earlier, as often occurs in clinical trials, especially in oncology. While methods accounting for dropouts have appeared in literature, they vary in assumptions, flexibility, and performance, and the differences between them are not widely understood. This manuscript aims to elucidate which methods can be used to handle VPC with dropout and when, along with a more informative VPC approach using confidence intervals. Additionally, we propose constructing the confidence interval based on the observed data instead of the simulated data. The theoretical framework for incorporating dropout in VPCs is developed and applied to propose two approaches: full and conditional. The full approach is implemented using a parametric time-to-event model, while the conditional approach is implemented using both parametric and Cox proportional-hazard (CPH) models. The practical performances of these approaches are illustrated with an application to the tumor growth dynamics (TGD) modeling of data from two cancer clinical trials of nivolumab and docetaxel, where patients were followed until disease progression. The dataset consisted of 3504 tumor size measurements from 855 subjects, which were described by a TGD model. The dropout of subjects was described by a Weibull or CPH model. Simulated datasets were also used to further illustrate the properties of the VPC methods. The results showed that the more familiar full approach might not provide meaningful improvement for TGD model evaluation over the naive approach of not adjusting for dropout, and could be outperformed by the conditional approach using either the Weibull model or the Cox proportional hazard model. Overall, including confidence intervals in VPC should improve interpretation, the conditional approach was shown to be more generally applicable when dropout occurs, and the nonparametric approach could provide additional robustness.

A population pharmacokinetic-pharmacodynamic model evaluating efficacy of nalbuphine extended-release in patients with prurigo nodularis

AIMS

Population pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PK-PD) models were used to describe the exposure-response (E-R) relationship between nalbuphine exposure and two widely used rating scales for itch: the Numerical Rating Scale for the subject's 'average'; itch experience (NRS-AV) and the Worst Itch (WI-NRS), with 24-h recall. Simulations based on the model E-R relationship were used to support dose selection for Phase 3 clinical trials and were evaluated with a target of reducing the 7-day average of the 24-h WI-NRS by at least 30% from baseline in most of the analysis population.

METHODS

Data from two clinical trials (NCT02373215: 9 healthy subjects; NCT02174419: 62 subjects with PN), in patients with prurigo nodularis (PN) with moderate to severe itch who received treatment with either of two doses of nalbuphine extended release (ER) or placebo, were used for the analysis. A two-compartment PK model with serial zero and first-order oral absorption was used to describe drug exposure. A maximum effect ( E max ) model with a placebo effect was used to model the itch response endpoints (NRS-AV, WI-NRS).

RESULTS

The PK-PD model predicted the exposure-related reduction in both NRS-AV and WI-NRS over time with approximately 63% and 27% of E max , respectively. Exposures associated with 80% of E max were achieved in about 78% of the patients at 162 mg, twice daily (BID), compared to 35% at 81 mg BID.

CONCLUSION

Simulated dose response indicated that 108 and 162 mg BID doses result in the highest proportion of patients achieving at least a 30% reduction in NRS-AV and WI-NRS, respectively.

Latent variable indirect response modeling of clinical efficacy endpoints with combination therapy: application to guselkumab and golimumab in patients with ulcerative colitis

Accurate characterization of longitudinal exposure-response of clinical trial endpoints is important in optimizing dose and dosing regimens in drug development. Clinical endpoints are often categorical, for which much progress has been made recently in latent variable indirect response (IDR) modeling with single drugs. However, such applications have not yet been used for trials employing multiple drugs administered concurrently. This study aims to demonstrate that the latent variable IDR approach provides a convenient longitudinal exposure-response modeling framework to assess potential interaction effects of combination therapies. This is illustrated by an application to the exposure-response modeling of guselkumab, a monoclonal antibody in clinical development that blocks the interleukin-23p19 subunit, and golimumab, a monoclonal antibody that binds with high affinity to tumor necrosis factor-alpha. A Phase 2a study was conducted in 214 patients with moderate-to severe active ulcerative colitis for which longitudinal assessments of disease severity based on patient-reported measures of rectal bleeding, stool frequency, and symptomatic remission were evaluated as categorical endpoints, and fecal calprotectin as a continuous endpoint. The modeling results suggested independent pharmacodynamic guselkumab and golimumab effects on fecal calprotectin as a continuous endpoint, as well as interaction effects on the categorical endpoints that may be explained by an additional pathway of competitive interaction.

Role of Disease Progression Models in Drug Development

The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided.

Improving categorical endpoint longitudinal exposure-response modeling through the joint modeling with a related endpoint

Exposure-response modeling is important to optimize dose and dosing regimens in clinical drug development. While primary clinical trial endpoints often have few categories and thus provide only limited information, sometimes there may be additional, more informative endpoints. Benefits of fully incorporating relevant information in longitudinal exposure-response modeling through joint modeling have recently been shown. This manuscript aims to further investigate the benefit of joint modeling of an ordered categorical primary endpoint with a related near-continuous endpoint, through the sharing of model parameters in the latent variable indirect response (IDR) modeling framework. This is illustrated by analyzing the data collected through up to 116 weeks from a phase 3b response-adaptive trial of ustekinumab in patients with psoriasis. The primary endpoint was based on the 6-point physician's global assessment (PGA) score. The Psoriasis area and severity Index (PASI) data, ranging from 0 to 72 with 0.1 increments, were also available. Separate and joint latent variable Type I IDR models of PGA and PASI scores were developed and compared. The results showed that the separate PGA model had a substantial structural bias, which was corrected by the joint modeling of PGA and PASI scores.

Population pharmacokinetics and exposure-response modeling analyses of guselkumab in patients with psoriatic arthritis

Guselkumab is an anti-interleukin-23 human monoclonal antibody effective in treating psoriatic arthritis (PsA). To characterize the pharmacokinetics (PKs) and exposure-response relationship of guselkumab in PsA, population PKs, and exposure-response modeling, analyses were conducted using data from pivotal phase III studies of subcutaneous guselkumab in patients with PsA. The observed serum concentration-time data of guselkumab were adequately described by a one-compartment linear PK model with first-order absorption and elimination. Covariates identified as contributing to the observed guselkumab PK variability were body weight and diabetes comorbidity; however, the magnitude of the effects of these covariates was not considered clinically relevant, and dose adjustment was not warranted for the patient population investigated. Positive exposure-response relationships were demonstrated with landmark and longitudinal exposure-response analyses between guselkumab exposure and clinical efficacy end points (American College of Rheumatology [ACR] 20%, 50%, and 70% improvement criteria and Investigator's Global Assessment [IGA] of psoriasis) at weeks 20 and/or 24, with no clinically relevant differences observed in improvement of PsA signs and symptoms between the two guselkumab treatment regimens evaluated (100 mg every 4 weeks or 100 mg at weeks 0 and 4, then every 8 weeks). Baseline Disease Activity Score in 28 joints (DAS28), Psoriasis Area and Severity Index (PASI) score, and/or C-reactive protein level were identified as influencing covariates on guselkumab exposure-response model parameters. These results provide a comprehensive evaluation of subcutaneous guselkumab PKs and exposure-response relationship that supports the dose regimen of 100 mg at weeks 0 and 4, then every 8 weeks in patients with PsA.

Exposure-response modeling of peficitinib efficacy in patients with rheumatoid arthritis

The aim was to analyze the relationship between peficitinib exposure and efficacy response according to American College of Rheumatology (ACR) 20 criteria and 28‐joint disease activity score based on C‐reactive protein (DAS28‐CRP) in rheumatoid arthritis (RA) patients, and to identify relevant covariates by developing exposure–response models. The analysis incorporated results from three multicenter, placebo‐controlled, double‐blind studies. As an exposure parameter, individual post hoc pharmacokinetic (PK) parameters were obtained from a previously constructed population PK model. Longitudinal ACR20 response rate and individual longitudinal DAS28‐CRP measurements were modeled by a non‐linear mixed effect model. Influential covariates were explored, and their effects on efficacy were quantitatively assessed and compared. The exposure–response models of effect of peficitinib on duration‐dependent increase in ACR20 response rate and decrease in DAS28‐CRP were adequately described by a continuous time Markov model and an indirect response model, respectively, with a sigmoidal E_max saturable of drug exposure in RA patients. The significant covariates were DAS28‐CRP and total bilirubin at baseline for the ACR20 response model, and CRP at baseline and concomitant methotrexate treatment for the DAS28–CRP model. The covariate effects were highly consistent between the two models. Our exposure–response models of peficitinib in RA patients satisfactorily described duration‐dependent improvements in ACR20 response rates and DAS28‐CRP measurements, and provided consistent covariate effects. Only the ACR20 model incorporated a patient's subjective high expectations just after the start of the treatment. Therefore, due to their similarities and differences, both models may have relevant applications in the development of RA treatment.

Clinical trial registration

NCT01649999 (RAJ1), NCT02308163 (RAJ3), NCT02305849 (RAJ4).

Exposure-Response Modeling to Support Dosing Selection for Phase IIb Development of Kukoamine B in Sepsis Patients

Aim: Kukoamine B, a small molecule compound, is being developed for the treatment of sepsis in a Phase II clinical trial. The objective of this study was to optimize dosing selection for a Phase IIb clinical trial using an exposure-response model.

Methods: Data of 34 sepsis patients from a Phase IIa clinical trial were used in the model: 10 sepsis patients from the placebo group and a total of 24 sepsis patients from the 0.06 mg/kg, 0.12 mg/kg, and 0.24 mg/kg drug groups. Exposure-response relationship was constructed to model the impact of the standard care therapy and area under curve (AUC) of kukoamine B to the disease biomarker (SOFA score). The model was evaluated by goodness of fit and visual predictive check. The simulation was performed 1,000 times based on the built model.

Results: The data of the placebo and the drug groups were pooled and modeled by a nonlinear mixed-effect modeling approach in sepsis. A latent-variable approach in conjunction with an inhibitory indirect response model was used to link the standard care therapy effect and drug exposure to SOFA score. The maximum fraction of the standard care therapy was estimated to 0.792. The eliminate rate constant of the SOFA score was 0.263/day for the standard care therapy. The production rate of SOFA score (K_in) was estimated at 0.0569/day and the AUC at half the maximal drug effect (EAUC₅₀) was estimated at 1,320 h*ng/mL. Model evaluation showed that the built model could well describe the observed SOFA score. Model-based simulations showed that the SOFA score on day 7 decreased to a plateau when AUC increased to 1,500 h*ng/mL.

Conclusion: We built an exposure-response model characterizing the pharmacological effect of kukoamine B from the standard care therapy in sepsis patients. A dose regimen of 0.24 mg/kg was finally recommended for the Phase IIb clinical trial of kukoamine B based on modeling and simulation results.

Application of Beta-Distribution and Combined Uniform and Binomial Methods in Longitudinal Modeling of Bounded Outcome Score Data

Disease status is often measured with bounded outcome scores (BOS) which takes a discrete set of values on a finite range. The distribution of such data is often skewed, rendering the standard analysis methods assuming normal distribution inappropriate. Among the methods used for BOS analyses, two of them have the ability to predict the data within its natural range and accommodate data skewness: (1) a recently proposed beta-distribution based approach and (2) a mixture model known as CUB (combined uniform and binomial). This manuscript compares the two approaches, using an established mechanism-based longitudinal exposure-response model to analyze data from a phase 2 clinical trial in psoriatic patients. The beta-distribution-based approach was confirmed to perform well, and CUB also showed potential. A separate issue of modeling clinical trial data is that the collected baseline disease score range may be more limited than that of post-treatment disease score due to clinical trial inclusion criteria, a fact that is typically ignored in longitudinal modeling. The effect of baseline disease status restriction should in principle be adjusted for in longitudinal modeling.

Facilitating Longitudinal Exposure-Response Modeling of a Composite Endpoint Using the Joint Modeling of Sparsely and Frequently Collected Subcomponents

Longitudinal exposure-response modeling plays an important role in optimizing dose and dosing regimens in clinical drug development. Certain clinical trials contain induction and maintenance phases where the maintenance treatment depends on the subjects' achieving the main endpoint outcome in the induction phase. Due to logistic difficulties and cost considerations, the main endpoint is usually collected more sparsely than a subcomponent (or other related endpoints). The sparse collection of the main endpoint hampers its longitudinal modeling. In principle, the frequent collection of a subcomponent allows its longitudinal modeling. However, the model evaluation via the visual predictive check (VPC) in the maintenance phase is difficult due to the requirement of the main-endpoint model to identify the treatment subgroups. This manuscript proposes a solution to this dilemma via the joint modeling of the main endpoint and the subcomponent. The challenges are illustrated by analyzing the data collected up to 60 weeks from a phase III trial of ustekinumab in patients with moderate-to-severe ulcerative colitis (UC). The main endpoint Mayo score, a commonly used composite endpoint to measure the severity of UC, was collected only at baseline, the end of the induction phase, and the end of the maintenance phase. The partial Mayo score, which is a major subset of the Mayo score, was collected at nearly every 4 weeks. A longitudinal joint exposure-response model, developed under a latent-variable Indirect Response modeling framework, described the Mayo score time course and facilitated the VPC model evaluation under a response-adaptive trial design.

Analysis of Longitudinal-Ordered Categorical Data for Muscle Spasm Adverse Event of Vismodegib: Comparison Between Different Pharmacometric Models

Longitudinal‐ordered categorical data, common in clinical trials, can be effectively analyzed with nonlinear mixed effect models. In this article, we systematically evaluated the performance of three different models in longitudinal muscle spasm adverse event (AE) data obtained from a clinical trial for vismodegib: a proportional odds (PO) model, a discrete‐time Markov model, and a continuous‐time Markov model. All models developed based on weekly spaced data can reasonably capture the proportion of AE grade over time; however, the PO model overpredicted the transition frequency between grades and the cumulative probability of AEs. The influence of data frequency (daily, weekly, or unevenly spaced) was also investigated. The PO model performance reduced with increased data frequency, and the discrete‐time Markov model failed to describe unevenly spaced data, but the continuous‐time Markov model performed consistently well. Clinical trial simulations were conducted to illustrate the muscle spasm resolution time profile during the 8‐week dose interruption period after 12 weeks of continuous treatment.

Handling underlying discrete variables with bivariate mixed hidden Markov models in NONMEM

Non-linear mixed effects models typically deal with stochasticity in observed processes but models accounting for only observed processes may not be the most appropriate for all data. Hidden Markov models (HMMs) characterize the relationship between observed and hidden variables where the hidden variables can represent an underlying and unmeasurable disease status for example. Adding stochasticity to HMMs results in mixed HMMs (MHMMs) which potentially allow for the characterization of variability in unobservable processes. Further, HMMs can be extended to include more than one observation source and are then multivariate HMMs. In this work MHMMs were developed and applied in a chronic obstructive pulmonary disease example. The two hidden states included in the model were remission and exacerbation and two observation sources were considered, patient reported outcomes (PROs) and forced expiratory volume (FEV1). Estimation properties in the software NONMEM of model parameters were investigated with and without random and covariate effect parameters. The influence of including random and covariate effects of varying magnitudes on the parameters in the model was quantified and a power analysis was performed to compare the power of a single bivariate MHMM with two separate univariate MHMMs. A bivariate MHMM was developed for simulating and analysing hypothetical COPD data consisting of PRO and FEV1 measurements collected every week for 60 weeks. Parameter precision was high for all parameters with the exception of the variance of the transition rate dictating the transition from remission to exacerbation (relative root mean squared error [RRMSE] > 150%). Parameter precision was better with higher magnitudes of the transition probability parameters. A drug effect was included on the transition rate probability and the precision of the drug effect parameter improved with increasing magnitude of the parameter. The power to detect the drug effect was improved by utilizing a bivariate MHMM model over the univariate MHMM models where the number of subject required for 80% power was 25 with the bivariate MHMM model versus 63 in the univariate MHMM FEV1 model and > 100 in the univariate MHMM PRO model. The results advocates for the use of bivariate MHMM models when implementation is possible.

On the Comparison of Methods in Analyzing Bounded Outcome Score Data

Clinical trial endpoints often take the form of bounded outcome scores (BOS) which report a discrete set of values on a finite range. Conceptually such endpoints are ordered categorical in nature, but in practice they are often analyzed as continuous variables, which may result in data range violations and difficulties to handle data skewness. Analysis methods dedicated for BOS data have been proposed; however, much confusion exists among pharmacometricians on how to compare the possible methods. This commentary reviews the main methods used in pharmacometrics applications and discusses their theoretical and practical comparisons. The expected performance of some conceptually appealing methods in different situations is discussed, and a guideline is provided on selecting analysis methods in practice.

Modeling near-continuous clinical endpoint as categorical: application to longitudinal exposure-response modeling of Mayo scores for golimumab in patients with ulcerative colitis

Accurate characterization of exposure-response relationship of clinical endpoints is important in drug development to identify optimal dose regimens. Endpoints with ≥ 10 ordered categories are typically analyzed as continuous. This manuscript aims to show circumstances where it is advantageous to analyze such data as ordered categorical. The results of continuous and categorical analyses are compared in a latent-variable based Indirect Response modeling framework for the longitudinal modeling of Mayo scores, ranging from 0 to 12, which is commonly used as a composite endpoint to measure the severity of ulcerative colitis (UC). Exposure response modeling of Mayo scores is complicated by the fact that studies typically include induction and maintenance phases with re-randomizations and other response-driven dose adjustments. The challenges are illustrated in this work by analyzing data collected from 3 phase II/III trials of golimumab in patients with moderate-to-severe UC. Visual predictive check was used for model evaluations. The ordered categorical approach is shown to be accurate and robust compared to the continuous approach. In addition, a disease progression model with an empirical bi-phasic rate of onset was found to be superior to the commonly used placebo model with one onset rate. An application of this modeling approach in guiding potential dose-adjustment was illustrated.

Exposure-Response Modeling Analyses for Sirukumab, a Human Monoclonal Antibody Targeting Interleukin 6, in Patients With Moderately to Severely Active Rheumatoid Arthritis

To characterize the dose-exposure-response relationship of sirukumab, an anti-interleukin 6 human monoclonal antibody, in the treatment of moderately to severely active rheumatoid arthritis (RA), we conducted exposure-response (E-R) modeling analyses based on data from two pivotal phase 3 placebo-controlled trials of sirukumab in patients with RA who were inadequate responders to nonbiologic disease-modifying antirheumatic drugs or anti-tumor necrosis factor α agents. A total of 2176 patients were included for the analyses and received subcutaneous administration of either placebo or sirukumab 50 mg every 4 weeks or 100 mg every 2 weeks. The clinical endpoints were 20%, 50%, and 70% improvement in the American College of Rheumatology response criteria (ie, ACR20, ACR50, and ACR70), and 28-joint Disease Activity Index Score (DAS28) using C-reactive protein. To provide a thorough assessment of the sirukumab E-R relationship, 2 pharmacokinetic/pharmacodynamic modeling approaches were implemented, including joint longitudinal modeling (ie, indirect response modeling of the time course of the 2 clinical endpoints) and landmark analyses (ie, direct linking of selected pharmacokinetic parameters to response at week 16 or 24). Results from both modeling analyses were generally consistent, and collectively suggested that the sirukumab subcutaneous dose of 50 mg every 4 weeks would produce near-maximal efficacy. No covariates identified in the E-R modeling analyses would have a significant impact on dose-response. Despite body weight and comorbid diabetes having significant effect on sirukumab exposure, simulations suggested that their effect on efficacy was small. Our work provides a comprehensive evaluation of sirukumab E-R to support dose recommendations in patients with RA.

Improvement in latent variable indirect response modeling of multiple categorical clinical endpoints: application to modeling of guselkumab treatment effects in psoriatic patients

Exposure-response modeling plays an important role in optimizing dose and dosing regimens during clinical drug development. The modeling of multiple endpoints is made possible in part by recent progress in latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript aims to investigate the level of improvement achievable by jointly modeling two such endpoints in the latent variable IDR modeling framework through the sharing of model parameters. This is illustrated with an application to the exposure-response of guselkumab, a human IgG1 monoclonal antibody in clinical development that blocks IL-23. A Phase 2b study was conducted in 238 patients with psoriasis for which disease severity was assessed using Psoriasis Area and Severity Index (PASI) and Physician's Global Assessment (PGA) scores. A latent variable Type I IDR model was developed to evaluate the therapeutic effect of guselkumab dosing on 75, 90 and 100% improvement of PASI scores from baseline and PGA scores, with placebo effect empirically modeled. The results showed that the joint model is able to describe the observed data better with fewer parameters compared with the common approach of separately modeling the endpoints.

Evaluation of estimation, prediction and inference for autocorrelated latent variable modeling of binary data-a simulation study

Longitudinal models of binary or ordered categorical data are often evaluated for adequacy by the ability of these to characterize the transition frequency and type between response states. Drug development decisions are often concerned with accurate prediction and inference of the probability of response by time and dose. A question arises on whether the transition probabilities need to be characterized adequately to ensure accurate response prediction probabilities unconditional on the previous response state. To address this, a simulation study was conducted to assess bias in estimation, prediction and inferences of autocorrelated latent variable models (ALVMs) when the transition probabilities are misspecified due to ill-posed random effects structures, inadequate likelihood approximation or omission of the autocorrelation component. The results may be surprising in that these suggest that characterizing autocorrelation in ALVMs is not as important as specifying a suitably rich random effects structure.

Improvement in latent variable indirect response joint modeling of a continuous and a categorical clinical endpoint in rheumatoid arthritis

Improving the quality of exposure-response modeling is important in clinical drug development. The general joint modeling of multiple endpoints is made possible in part by recent progress on the latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript aims to investigate, when modeling a continuous and a categorical clinical endpoint, the level of improvement achievable by joint modeling in the latent variable IDR modeling framework through the sharing of model parameters for the individual endpoints, guided by the appropriate representation of drug and placebo mechanism. This was illustrated with data from two phase III clinical trials of intravenously administered mAb X for the treatment of rheumatoid arthritis, with the 28-joint disease activity score (DAS28) and 20, 50, and 70% improvement in the American College of Rheumatology (ACR20, ACR50, and ACR70) disease severity criteria were used as efficacy endpoints. The joint modeling framework led to a parsimonious final model with reasonable performance, evaluated by visual predictive check. The results showed that, compared with the more common approach of separately modeling the endpoints, it is possible for the joint model to be more parsimonious and yet better describe the individual endpoints. In particular, the joint model may better describe one endpoint through subject-specific random effects that would not have been estimable from data of this endpoint alone.

Exposure-outcome analysis in depressed patients treated with paroxetine using population pharmacokinetics

Purpose

This study investigated population pharmacokinetics of paroxetine, and then performed an integrated analysis of exposure and clinical outcome using population pharmacokinetic parameter estimates in depressed patients treated with paroxetine.

Patients and methods

A total of 271 therapeutic drug monitoring (TDM) data were retrospectively collected from 127 psychiatric outpatients. A population nonlinear mixed-effects modeling approach was used to describe serum concentrations of paroxetine. For 83 patients with major depressive disorder, the treatment response rate and the incidence of adverse drug reaction (ADR) were characterized by logistic regression using daily dose or area under the concentration–time curve (AUC) estimated from the final model as a potential exposure predictor.

Results

One compartment model was developed. The apparent clearance of paroxetine was affected by age as well as daily dose administered at steady-state. Overall treatment response rate was 72%, and the incidence of ADR was 30%. The logistic regression showed that exposure predictors were not associated with treatment response or ADR in the range of dose commonly used in routine practice. However, the incidence of ADR increased with the increase of daily dose or AUC for the patients with multiple concentrations.

Conclusion

In depressed patients treated with paroxetine, TDM may be of limited value for individualization of treatment.

Establishing Good Practices for Exposure-Response Analysis of Clinical Endpoints in Drug Development

This tutorial aims at promoting good practices for exposure–response (E-R) analyses of clinical endpoints in drug development. The focus is on practical aspects of E-R analyses to assist modeling scientists with a process of performing such analyses in a consistent manner across individuals and projects and tailored to typical clinical drug development decisions. This includes general considerations for planning, conducting, and visualizing E-R analyses, and how these are linked to key questions.