Development of visual predictive checks accounting for multimodal parameter distributions in mixture models

The assumption of interindividual variability being unimodally distributed in nonlinear mixed effects models does not hold when the population under study displays multimodal parameter distributions. Mixture models allow the identification of parameters characteristic to a subpopulation by describing these multimodalities. Visual predictive check (VPC) is a standard simulation based diagnostic tool, but not yet adapted to account for multimodal parameter distributions. Mixture model analysis provides the probability for an individual to belong to a subpopulation (IPmix) and the most likely subpopulation for an individual to belong to (MIXEST). Using simulated data examples, two implementation strategies were followed to split the data into subpopulations for the development of mixture model specific VPCs. The first strategy splits the observed and simulated data according to the MIXEST assignment. A shortcoming of the MIXEST-based allocation strategy was a biased allocation towards the dominating subpopulation. This shortcoming was avoided by splitting observed and simulated data according to the IPmix assignment. For illustration purpose, the approaches were also applied to an irinotecan mixture model demonstrating 36% lower clearance of irinotecan metabolite (SN-38) in individuals with UGT1A1 homo/heterozygote versus wild-type genotype. VPCs with segregated subpopulations were helpful in identifying model misspecifications which were not evident with standard VPCs. The new tool provides an enhanced power of evaluation of mixture models

Comparisons of analysis methods for assessment of pharmacodynamic interactions including design recommendations

Quantitative evaluation of potential pharmacodynamic (PD) interactions is important in tuberculosis drug development in order to optimize Phase 2b drug selection and ultimately to define clinical combination regimens. In this work, we used simulations to (1) evaluate different analysis methods for detecting PD interactions between two hypothetical anti-tubercular drugs in in vitro time-kill experiments, and (2) provide design recommendations for evaluation of PD interactions. The model used for all simulations was the Multistate Tuberculosis Pharmacometric (MTP) model linked to the General Pharmacodynamic Interaction (GPDI) model. Simulated data were re-estimated using the MTP–GPDI model implemented in Bliss Independence or Loewe Additivity, or using a conventional model such as an Empirical Bliss Independence-based model or the Greco model based on Loewe Additivity. The GPDI model correctly characterized different PD interactions (antagonism, synergism, or asymmetric interaction), regardless of the underlying additivity criterion. The commonly used conventional models were not able to characterize asymmetric PD interactions, i.e., concentration-dependent synergism and antagonism. An optimized experimental design was developed that correctly identified interactions in ≥ 94% of the evaluated scenarios using the MTP–GPDI model approach. The MTP–GPDI model approach was proved to provide advantages to other conventional models for assessing PD interactions of anti-tubercular drugs and provides key information for selection of drug combinations for Phase 2b evaluation

Variability Attribution for Automated Model Building

We investigated the possible advantages of using linearization to evaluate models of residual unexplained variability (RUV) for automated model building in a similar fashion to the recently developed method “residual modeling.” Residual modeling, although fast and easy to automate, cannot identify the impact of implementing the needed RUV model on the imprecision of the rest of model parameters. We used six RUV models to be tested with 12 real data examples. Each example was first linearized; then, we assessed the agreement in improvement of fit between the base model and its extended models for linearization and conventional analysis, in comparison to residual modeling performance. Afterward, we compared the estimates of parameters’ variabilities and their uncertainties obtained by linearization to conventional analysis. Linearization accurately identified and quantified the nature and magnitude of RUV model misspecification similar to residual modeling. In addition, linearization identified the direction of change and quantified the magnitude of this change in variability parameters and their uncertainties. This method is implemented in the software package PsN for automated model building/evaluation with continuous data

Model-Based Residual Post-Processing for Residual Model Identification

The purpose of this study was to investigate if model-based post-processing of common diagnostics can be used as a diagnostic tool to quantitatively identify model misspecifications and rectifying actions. The main investigated diagnostic is conditional weighted residuals (CWRES). We have selected to showcase this principle with residual unexplained variability (RUV) models, where the new diagnostic tool is used to scan extended RUV models and assess in a fast and robust way whether, and what, extensions are expected to provide a superior description of data. The extended RUV models evaluated were autocorrelated errors, dynamic transform both sides, inter-individual variability on RUV, power error model, t-distributed errors, and time-varying error magnitude. The agreement in improvement in goodness-of-fit between implementing these extended RUV models on the original model and implementing these extended RUV models on CWRES was evaluated in real and simulated data examples. Real data exercise was applied to three other diagnostics: conditional weighted residuals with interaction (CWRESI), individual weighted residuals (IWRES), and normalized prediction distribution errors (NPDE). CWRES modeling typically predicted (i) the nature of model misspecifications, (ii) the magnitude of the expected improvement in fit in terms of difference in objective function value (Delta OFV), and (iii) the parameter estimates associated with the model extension. Alternative metrics (CWRESI, IWRES, and NPDE) also provided valuable information, but with a lower predictive performance of Delta OFV compared to CWRES. This method is a fast and easily automated diagnostic tool for RUV model development/evaluation process; it is already implemented in the software package PsN

Long Term Norovirus Infection in a Patient with Severe Common Variable Immunodeficiency

Norovirus is the most common cause of acute non-bacterial gastroenteritis. Immunocompromised patients can become chronically infected, with or without symptoms. In Europe, common variable immunodeficiency (CVID) is one of the most common inborn errors of immunity. A potentially severe complication is CVID-associated enteropathy, a disorder with similar histopathology to celiac disease. Studies suggest that chronic norovirus infection may be a contributor to CVID enteropathy, and that the antiviral drug ribavirin can be effective against norovirus. Here, a patient with CVID-like disease with combined B- and T-cell deficiency, had chronic norovirus infection and enteropathy. The patient was routinely administered subcutaneous and intravenous immunoglobulin replacement therapy (SCIg and IVIg). The patient was also administered ribavirin for -7.5 months to clear the infection. Stool samples (collected 2013-2016) and archived paraffin embedded duodenal biopsies were screened for norovirus by qPCR, confirming a chronic infection. Norovirus genotyping was done in 25 stool samples. For evolutionary analysis, the capsid (VP1) and polymerase (RdRp) genes were sequenced in 10 and 12 stool samples, respectively, collected before, during, and after ribavirin treatment. Secretor phenotyping was done in saliva, and serum was analyzed for histoblood group antigen (HBGA) blocking titers. The chronic norovirus strain formed a unique variant subcluster, with GII.4 Den Haag [P4] variant, circulating around 2009, as the most recent common ancestor. This corresponded to the documented debut of symptoms. The patient was a secretor and had HBGA blocking titers associated with protection in immunocompetent individuals. Several unique amino acid substitutions were detected in immunodominant epitopes of VP1. However, HBGA binding sites were conserved. Ribavirin failed in treating the infection and no clear association between ribavirin-levels and quantity of norovirus shedding was observed. In conclusion, long term infection with norovirus in a patient with severe CVID led to the evolution of a unique norovirus strain with amino acid substitutions in immunodominant epitopes, but conservation within HBGA binding pockets. Regularly administered SCIg, IVIg, and similar to 7.5-month ribavirin treatment failed to clear the infection.Funding Agencies|Region Ostergotland [ALF-LIU-934451]; Karolinska Institute [ALF-KI-K23003073]; Swedish Research Council [2018-02862]</p

The Standard Output : A Tool-Agnostic Modeling Storage Format

New standards have been recently defined and implemented enabling a reliable exchange of pharmacometric models across software tools, and facilitating collaborative drug and disease modeling and simulation (M&S) activities. Among these, the Standard Output (SO) has been proposed as the tool‐independent exchange and storage format for typical M&S results. The SO integration within the Drug Disease Model Resource (DDMoRe) interoperability framework (IOF) has already shown its potential for enabling effective data flow across modeling tasks and facilitating information retrieval

Model Description Language (MDL): A Standard for Modeling and Simulation

Recent work on Model Informed Drug Discovery and Development (MID3) has noted the need for clarity in model description used in quantitative disciplines such as pharmacology and statistics.1-3 Currently, models are encoded in a variety of computer languages and are shared through publications that rarely include original code and generally lack reproducibility. The DDMoRe Model Description Language (MDL) has been developed primarily as a language standard to facilitate sharing knowledge and understanding of models