Search

Increasing complexity of mAbs in development creates challenges in predicting human pharmacokinetic (PK) parameters from preclinical data. The aim of this analysis was to identify optimal allometric scaling exponents. Data were extracted from literature to create a central database (currently the largest available published database) of two-compartment model parameters for mAbs (n = 59) in cynomolgus monkey (CM) and human. Global allometric exponents were calculated and drug-dependent factors were investigated as potential variables in determining the optimal scaling factor. The global exponents for scaling CM mAb PK data were 0.74 (CL), 0.80 (CL with Fc-modified mAbs excluded), 0.44 (CL with Fc-modified mAbs only), 0.71 (Q), 1.12 (V1), and 0.99 (V2). These values are in line with previously published literature values. [ABSTRACT FROM AUTHOR]

Dose–response analysis is often applied to the quantification of drug‐effect especially for slowly responding disease end points where a comparison is made across dose levels after a particular period of treatment. It has long been recognized that exposure – response is more appropriate than dose–response. However, trials necessarily are designed as dose–response experiments. Second, a wide range of functional forms are used to express relationships between dose and response. These considerations are also important for clinical development because pharmacokinetic (PK; and variability) plus pharmacokinetic‐pharmacodynamic modeling may allow one to anticipate the shape of the dose–response curve and so the trial design. Here, we describe how the location and steepness of the dose response is determined by the PKs of the compound being tested and its exposure‐response relationship in terms of potency (location), efficacy (maximum effect) and Hill coefficient (steepness). Thus, the location (50% effective dose [ED50]) is dependent not only on the potency (half‐maximal effective concentration) but also the compound's PKs. Similarly, the steepness of the dose response is shown to be a function of the half‐life of the drug. It is also shown that the shape of relationship varies dependent on the assumed time course of the disease. This is important in the context of drug‐discovery where the in vivo potencies of compounds are compared as well as when considering an analysis of summary data (for example, model‐based meta‐analysis) for clinical decision making. [ABSTRACT FROM AUTHOR]

In this study we compare seven mathematical models of tumour growth using nonlinear mixed-effects which allows for a simultaneous fitting of multiple data and an estimation of both mean behaviour and variability. This is performed for two large datasets, a patient-derived xenograft (PDX) dataset consisting of 220 PDXs spanning six different tumour types and a cell-line derived xenograft (CDX) dataset consisting of 25 cell lines spanning eight tumour types. Comparison of the models is performed by means of visual predictive checks (VPCs) as well as the Akaike Information Criterion (AIC). Additionally, we fit the models to 500 bootstrap samples drawn from the datasets to expand the comparison of the models under dataset perturbations and understand the growth kinetics that are best fitted by each model. Through qualitative and quantitative metrics the best models are identified the effectiveness and practicality of simpler models is highlighted [ABSTRACT FROM AUTHOR]

Epstein-Barr virus (EBV) establishes a lifelong latent infection in healthy humans, kept under immune control by cytotoxic T cells (CTLs). Following paediatric haematopoetic stem cell transplantation (HSCT), a loss of immune surveillance leads to opportunistic outgrowth of EBV-infected cells, resulting in EBV reactivation, which can ultimately progress to post-transplant lymphoproliferative disorder (PTLD). The aims of this study were to identify risk factors for EBV reactivation in children in the first 100 days post-HSCT and to assess the suitability of a previously reported mathematical model to mechanistically model EBV reactivation kinetics in this cohort. Retrospective electronic data were collected from 56 children who underwent HSCT at Great Ormond Street Hospital (GOSH) between 2005 and 2016. Using EBV viral load (VL) measurements from weekly quantitative PCR (qPCR) monitoring post-HSCT, a multivariable Cox proportional hazards (Cox-PH) model was developed to assess time to first EBV reactivation event in the first 100 days post-HSCT. Sensitivity analysis of a previously reported mathematical model was performed to identify key parameters affecting EBV VL. Cox-PH modelling revealed EBV seropositivity of the HSCT recipient and administration of anti-thymocyte globulin (ATG) pre-HSCT to be significantly associated with an increased risk of EBV reactivation in the first 100 days post-HSCT (adjusted hazard ratio (AHR) = 2.32, P = 0.02; AHR = 2.55, P = 0.04). Five parameters were found to affect EBV VL in sensitivity analysis of the previously reported mathematical model. In conclusion, we have assessed the effect of multiple covariates on EBV reactivation in the first 100 days post-HSCT in children and have identified key parameters in a previously reported mechanistic mathematical model that affect EBV VL. Future work will aim to fit this model to patient EBV VLs, develop the model to account for interindividual variability and model the effect of clinically relevant covariates such as rituximab therapy and ATG on EBV VL. [ABSTRACT FROM AUTHOR]

Translational model‐based approaches have played a role in increasing success in the development of novel anticancer treatments. However, despite this, significant translational uncertainty remains from animal models to patients. Optimization of dose and scheduling (regimen) of drugs to maximize the therapeutic utility (maximize efficacy while avoiding limiting toxicities) is still predominately driven by clinical investigations. Here, we argue that utilizing pragmatic mechanism‐based translational modeling of nonclinical data can further inform this optimization. Consequently, a prototype model is demonstrated that addresses the required fundamental mechanisms. [ABSTRACT FROM AUTHOR]

Mathematical models in oncology aid in the design of drugs and understanding of their mechanisms of action by simulation of drug biodistribution, drug effects, and interaction between tumor and healthy cells. The traditional approach in pharmacometrics is to develop and validate ordinary differential equation models to quantify trends at the population level. In this approach, time‐course of biological measurements is modeled continuously, assuming a homogenous population. Another approach, agent‐based models, focuses on the behavior and fate of biological entities at the individual level, which subsequently could be summarized to reflect the population level. Heterogeneous cell populations and discrete events are simulated, and spatial distribution can be incorporated. In this tutorial, an agent‐based model is presented and compared to an ordinary differential equation model for a tumor efficacy model inhibiting the pERK pathway. We highlight strengths, weaknesses, and opportunities of each approach. [ABSTRACT FROM AUTHOR]

A key aim of early clinical development for new cancer treatments is to detect the potential for efficacy early and to identify a safe therapeutic dose to take forward to phase II. Because of this need, researchers have sought to build mathematical models linking initial radiologic tumor response, often assessed after 6 to 8 weeks of treatment, with overall survival. However, there has been mixed success of this approach in the literature. We argue that evolutionary selection pressure should be considered to interpret these early efficacy signals and so optimize cancer therapy. [ABSTRACT FROM AUTHOR]

The lack of standardization in the way that quantitative and systems pharmacology (QSP) models are developed, tested, and documented hinders their reproducibility, reusability, and expansion or reduction to alternative contexts. This in turn undermines the potential impact of QSP in academic, industrial, and regulatory frameworks. This article presents a minimum set of recommendations from the UK Quantitative and Systems Pharmacology Network (UK QSP Network) to guide QSP practitioners seeking to maximize their impact, and stakeholders considering the use of QSP models in their environment. [ABSTRACT FROM AUTHOR]

Purpose: Epidermal growth factor receptor (EGFR) is thought to play a role in the regulation of cell proliferation; with its activation stimulating tumour growth. EGFR inhibitors have shown promise in the treatment of cancer, particularly in non-small cell lung cancer, however, resistance is observed in the majority of patients. A tumour growth model was developed aiming to explain this resistance.Methods: The model incorporating populations of both sensitive and resistant cells were fitted to data from a study of EGFR inhibitor AZD3759 in brain metastasis mouse models. The observed regrowth of tumours in higher dose groups suggested the development of resistance to treatment. The bioluminescence observations were highly variable, covering many orders of magnitude, so to assess how reliable the model was, the parameter estimates were compared to those found in less noisy subcutaneous mouse models.Results: The fitted model suggested that resistance was mainly due to a proportion of cells being resistant at baseline, and the contribution of mutations occurring during the study leading to resistance was negligible. Estimated growth rate and dose-response was found to be comparable between brain metastasis and subcutaneous mouse models.Conclusions: The developed model to describe resistance suggests that the resistance to EGFR-inhibition seen in these xenografts is best described by assuming a small percentage of cells are resistant to treatment at baseline. This model suggests changes to dosing and dosing schedule may not prevent resistance to treatment developing, and that additional treatments would need to be used in combination to overcome resistance. [ABSTRACT FROM AUTHOR]

Balancing antitumor efficacy with toxicity is a significant challenge, and drug‐induced myelosuppression is a common dose‐limiting toxicity of cancer treatments. Mathematical modeling has proven to be a powerful ally in this field, scaling results from animal models to humans, and designing optimized treatment regimens. Here we outline existing mathematical approaches for studying bone marrow toxicity, identify gaps in current understanding, and make future recommendations to advance this vital field of safety research further. [ABSTRACT FROM AUTHOR]

Background and Purpose: AZD8055 is a potent orally available mTOR kinase inhibitor with in vitro and in vivo antitumour activity against a range of tumour types. Preclinical studies showed that AZD8055 induced a dose-dependent pharmacodynamic effect in xenograft models in vivo, but a lack of understanding of the relative contributions of the maximum inhibition of the biomarkers and the duration of inhibition to the antitumour effect, limited the rational design of experiments to optimize the dose and schedules of treatment.Experimental Approach: In this study, a mathematical modelling approach was developed to relate pharmacodynamics and antitumour activity using preclinical data generated in mice bearing U87-MG xenografts.Key Results: Refinement and validation of the model was carried out in a panel of additional human tumour xenograft models with different growth rates and different sensitivity to AZD8055 (from partial growth inhibition to regression). Finally, the model was applied to accurately predict the efficacy of high, intermittent dosing schedules of AZD8055.Conclusions and Implications: Overall, this new model linking pharmacokinetics, pharmacodynamic biomarkers and efficacy across several tumour xenografts with different sensitivity to AZD8055 was able to identify the optimal dose and route of administration to maximize the antitumour efficacy in preclinical models and its potential for translation into man. [ABSTRACT FROM AUTHOR]

To advance the systems approach in pharmacology, experimental models and computational methods need to be integrated from early drug discovery onward. Here, we propose outside‐in model development, a model identification technique to understand and predict the dynamics of a system without requiring prior biological and/or pharmacological knowledge. The advanced data required could be obtained by whole vertebrate, high‐throughput, low‐resource dose‐exposure‐effect experimentation with the zebrafish larva. Combinations of these innovative techniques could improve early drug discovery. [ABSTRACT FROM AUTHOR]

Xenografts -as simplified animal models of cancer- differ substantially in vasculature and stromal architecture when compared to clinical tumours. This makes mathematical model-based predictions of clinical outcome challenging. Our objective is to further understand differences in tumour progression and physiology between animal models and the clinic. To achieve that, we propose a mathematical model based upon tumour pathophysiology, where oxygen -as a surrogate for endocrine delivery- is our main focus. The Oxygen-Driven Model (ODM), using oxygen diffusion equations, describes tumour growth, hypoxia and necrosis. The ODM describes two key physiological parameters. Apparent oxygen uptake rate () represents the amount of oxygen cells seem to need to proliferate. The more oxygen they appear to need, the more the oxygen transport. gathers variability from the vasculature, stroma and tumour morphology. Proliferating rate (kp) deals with cell line specific factors to promote growth. The KH,KN describe the switch of hypoxia and necrosis. Retrospectively, using archived data, we looked at longitudinal tumour volume datasets for 38 xenografted cell lines and 5 patient-derived xenograft-like models. Exploration of the parameter space allows us to distinguish 2 groups of parameters. Group 1 of cell lines shows a spread in values of and lower kp, indicating that tumours are poorly perfused and slow growing. Group 2 share the value of the oxygen uptake rate () and vary greatly in kp, which we interpret as having similar oxygen transport, but more tumour intrinsic variability in growth. However, the ODM has some limitations when tested in explant-like animal models, whose complex tumour-stromal morphology may not be captured in the current version of the model. Incorporation of stroma in the ODM will help explain these discrepancies. We have provided an example. The ODM is a very simple -and versatile- model suitable for the design of preclinical experiments, which can be modified and enhanced whilst maintaining confidence in its predictions. [ABSTRACT FROM AUTHOR]

1. To investigate the non-linear kinetics of in vitro hepatocyte uptake across species, the OATP substrate Pitavastatin was used as a probe. 2. Experiments were conducted at AstraZeneca (Alderley Park, Macclesfield) using freshly isolated rat, dog and human hepatocytes, utilising the 'oil spin' methodology described by Hassen et al. (1996). Very few mechanistic models have previously been used to characterise the uptake process. 3. Here two candidate pharmacokinetic non-linear compartmental models are proposed. Both models have been shown to be structurally identifiable and distinghishable previously, which establishes that all unknown parameters could be identified from the experimental observations available and that input/output relationships for both the candidate models were structurally different. 4. A kinetic modelling software package, FACSIMILE (MCPA Software, Faringdon, UK), was used to obtain numerical solutions for the system equations and for parameter estimation. Model fits gave good agreement with the in vitro data and suggest the current widely accepted assumption that the rate of diffusion across the hepatocyte cell membrane is the same at both 4 °C and 37 °C is not valid, at least for Pitavastatin. Although this finding has already been proposed, this is the first time it is comprehensively debunked using statistical testing. [ABSTRACT FROM AUTHOR]

The volume of distribution at steady state is considered to be one of the primary pharmacokinetic measurements obtained from in vivo experiments. This quantity is quite commonly calculated using moments of the observed concentration curve, the process being referred to as noncompartmental analysis. In this paper the underlying assumptions of noncompartmental analysis are analysed with regard to the observed behaviour of models with two compartments: one of which has elimination from the central compartment, the other from the peripheral tissue compartment. This is in order to clarify the relationship between volume of distribution and clearance. It is shown that these two models are indistinguishable from measurements in blood. Furthermore relationships between the parameter values for the two models are given so that they produce the same observed profile. Expressions are derived in a novel way that relates the volume of distribution to these model rate constants. The definitions of clearance and volume of distribution at steady state are investigated using several different mathematical techniques, demonstrating the consistency of the derived expressions. It is shown, in a manner that the authors believe is a new approach, that when the assumption of central elimination does not apply, noncompartmental analysis will under estimate the volume of distribution, whereas clearance remains unchanged. This is compared quantitatively with respect to the volume predicted where central elimination holds, and is a result of an extended mean residence time. © 2007 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 97:111–122, 2008 [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the performance of various empirical, semimechanistic and mechanistic methodologies with and without protein binding corrections for the prediction of human volume of distribution at steady state ( Vss). PhRMA member companies contributed a set of blinded data from preclinical and clinical studies, and 18 drugs with intravenous clinical pharmacokinetics (PK) data were available for the analysis . In vivo and in vitro preclinical data were used to predict Vss by 24 different methods. Various statistical and outlier techniques were employed to assess the predictability of each method. There was not simply one method that predicts Vss accurately for all compounds. Across methods, the maximum success rate in predicting human Vss was 100%, 94%, and 78% of the compounds with predictions falling within tenfold, threefold, and twofold error, respectively, of the observed Vss. Generally, the methods that made use of in vivo preclinical data were more predictive than those methods that relied solely on in vitro data. However, for many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. It is recommended to initially use the in vitro tissue composition-based equations to predict Vss in preclinical species and humans, putting the assumptions and compound properties into context. As in vivo data become available, these predictions should be reassessed and rationalized to indicate the level of confidence (uncertainty) in the human Vss prediction. The top three methods that perform strongly at integrating in vivo data in this way were the Øie-Tozer, the rat -dog-human proportionality equation, and the lumped-PBPK approach. Overall, the scientific benefit of this study was to obtain greater characterization of predictions of human Vss from several methods available in the literature. © 2011 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:4074-4089, 2011 [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the performance of various allometric and in vitro- in vivo extrapolation (IVIVE) methodologies with and without plasma protein binding corrections for the prediction of human intravenous (i.v.) clearance (CL). The objective was also to evaluate the IVIVE prediction methods with animal data. Methodologies were selected from the literature. Pharmaceutical Research and Manufacturers of America member companies contributed blinded datasets from preclinical and clinical studies for 108 compounds, among which 19 drugs had i.v. clinical pharmacokinetics data and were used in the analysis. In vivo and in vitro preclinical data were used to predict CL by 29 different methods. For many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. In addition, 66 methods of predicting oral (p.o.) area under the curve (AUCp.o.) were evaluated for 107 compounds using rational combinations of i.v. CL and bioavailability ( F), and direct scaling of observed p.o. CL from preclinical species. Various statistical and outlier techniques were employed to assess the predictability of each method. Across methods, the maximum success rate in predicting human CL for the 19 drugs was 100%, 94%, and 78% of the compounds with predictions falling within 10-fold, threefold, and twofold error, respectively, of the observed CL. In general, in vivo methods performed slightly better than IVIVE methods (at least in terms of measures of correlation and global concordance), with the fu intercept method and two-species-based allometry (rat-dog) being the best performing methods. IVIVE methods using microsomes (incorporating both plasma and microsomal binding) and hepatocytes (not incorporating binding) resulted in 75% and 78%, respectively, of the predictions falling within twofold error. IVIVE methods using other combinations of binding assumptions were much less accurate. The results for prediction of AUCp.o. were consistent with i.v. CL. However, the greatest challenge to successful prediction of human p.o. CL is the estimate of F in human. Overall, the results of this initiative confirmed predictive performance of common methodologies used to predict human CL. © 2011 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:4090-4110, 2011 [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the performance of the Wajima allometry ( Css-MRT) approach published in the literature, which is used to predict the human plasma concentration-time profiles from a scaling of preclinical species data. A diverse and blinded dataset of 108 compounds from PhRMA member companies was used in this evaluation. The human intravenous (i.v.) and oral (p.o.) pharmacokinetics (PK) data were available for 18 and 107 drugs, respectively. Three different scenarios were adopted for prediction of human PK profiles. In the first scenario, human clearance (CL) and steady-state volume of distribution ( Vss) were predicted by unbound fraction corrected intercept method (FCIM) and Øie-Tozer (OT) approaches, respectively. Quantitative structure activity relationship (QSAR)-based approaches (TSrat-dog) based on compound descriptors together with rat and dog data were utilized in the second scenario. Finally, in the third scenario, CL and Vss were predicted using the FCIM and Jansson approaches, respectively. For the prediction of oral pharmacokinetics, the human bioavailability and absorption rate constant were assumed as the average of preclinical species. Various statistical techniques were used for assessing the accuracy of the simulation scenarios. The human CL and Vss were predicted within a threefold error range for about 75% of the i.v. drugs. However, the accuracy in predicting key p.o. PK parameters appeared to be lower with only 58% of simulations falling within threefold of observed parameters. The overall ability of the Css-MRT approach to predict the curve shape of the profile was in general poor and ranged between low to medium level of confidence for most of the predictions based on the selected criteria. © 2011 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:4111-4126, 2011 [ABSTRACT FROM AUTHOR]

The objective of this study is to assess the effectiveness of physiologically based pharmacokinetic (PBPK) models for simulating human plasma concentration-time profiles for the unique drug dataset of blinded data that has been assembled as part of a Pharmaceutical Research and Manufacturers of America initiative. Combinations of absorption, distribution, and clearance models were tested with a PBPK approach that has been developed from published equations. An assessment of the quality of the model predictions was made on the basis of the shape of the plasma time courses and related parameters. Up to 69% of the simulations of plasma time courses made in human demonstrated a medium to high degree of accuracy for intravenous pharmacokinetics, whereas this number decreased to 23% after oral administration based on the selected criteria. The simulations resulted in a general underestimation of drug exposure ( Cmax and AUC0- t). The explanations for this underestimation are diverse. Therefore, in general it may be due to underprediction of absorption parameters and/or overprediction of distribution or oral first-pass. The implications of compound properties are demonstrated. The PBPK approach based on in vitro-input data was as accurate as the approach based on in vivo data. Overall, the scientific benefit of this modeling study was to obtain more extensive characterization of predictions of human PK from PBPK methods. © 2011 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:4127-4157, 2011 [ABSTRACT FROM AUTHOR]

This study is part of the Pharmaceutical Research and Manufacturers of America (PhRMA) initiative on predictive models of efficacy, safety, and compound properties. The overall goal of this part was to assess the predictability of human pharmacokinetics (PK) from preclinical data and to provide comparisons of available prediction methods from the literature, as appropriate, using a representative blinded dataset of drug candidates. The key objectives were to (i) appropriately assemble and blind a diverse dataset of in vitro, preclinical in vivo, and clinical data for multiple drug candidates, (ii) evaluate the dataset with empirical and physiological methodologies from the literature used to predict human PK properties and plasma concentration-time profiles, (iii) compare the predicted properties with the observed clinical data to assess the prediction accuracy using routine statistical techniques and to evaluate prediction method(s) based on the degree of accuracy of each prediction method, and (iv) compile and summarize results for publication. Another objective was to provide a mechanistic understanding as to why one methodology provided better predictions than another, after analyzing the poor predictions. A total of 108 clinical lead compounds were collected from 12 PhRMA member companies. This dataset contains intravenous ( n = 19) and oral pharmacokinetic data ( n = 107) in humans as well as the corresponding preclinical in vitro, in vivo, and physicochemical data. All data were blinded to protect the anonymity of both the data and the company submitting the data. This manuscript, which is the first of a series of manuscripts, summarizes the PhRMA initiative and the 108 compound dataset. More details on the predictability of each method are reported in companion manuscripts. © 2011 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:4050-4073, 2011 [ABSTRACT FROM AUTHOR]

As pharmaceutical development moves from early-stage in vitro experimentation to later in vivo and subsequent clinical trials, data and knowledge are acquired across multiple time and length scales, from the subcellular to whole patient cohort scale. Realizing the potential of this data for informing decision making in pharmaceutical development requires the individual and combined application of machine learning (ML) and mechanistic multiscale mathematical modeling approaches. Here we outline how these two approaches, both individually and in tandem, can be applied at different stages of the drug discovery and development pipeline to inform decision making compound development. The importance of discerning between knowledge and data are highlighted in informing the initial use of ML or mechanistic quantitative systems pharmacology (QSP) models. We discuss the application of sensitivity and structural identifiability analyses of QSP models in informing future experimental studies to which ML may be applied, as well as how ML approaches can be used to inform mechanistic model development. Relevant literature studies are highlighted and we close by discussing caveats regarding the application of each approach in an age of constant data acquisition. SIGNIFICANCE STATEMENT: We consider when best to apply machine learning (ML) and mechanistic quantitative systems pharmacology (QSP) approaches in the context of the drug discovery and development pipeline. We discuss the importance of prior knowledge and data available for the system of interest and how this informs the individual and combined application of ML and QSP approaches at each stage of the pipeline., (Copyright © 2023 by The American Society for Pharmacology and Experimental Therapeutics.)

Mathematical modelling has made significant contributions to the optimization of the use of antimicrobial treatments. This article discusses the key processes that such mathematical modelling should attempt to capture. In particular, this article highlights that the response of the host immune system requires quantification, and this is illustrated with a novel model structure., Competing Interests: Competing of Interests None declared., (Copyright © 2022 Elsevier Ltd and International Society of Antimicrobial Chemotherapy. All rights reserved.)

Purpose: Cancer disease burden is commonly assessed radiologically in solid tumours in support of response assessment via the RECIST criteria. These longitudinal data are amenable to mathematical modelling and these models characterise the initial tumour size, initial tumour shrinkage in responding patients and rate of regrowth as patient's disease progresses. Knowing how these parameters vary between patient populations and treatments would inform translational modelling approaches from non-clinical data as well as clinical trial design., Experimental Design: Here a meta-analysis of reported model parameter values is reported. Appropriate literature was identified via a PubMed search and the application of text-based clustering approaches. The resulting parameter estimates are examined graphically and with ANOVA., Results: Parameter values from a total of 80 treatment arms were identified based on 80 trial arms containing a total of 34,881 patients. Parameter estimates are generally consistent. It is found that a significant proportion of the variation in rates of tumour shrinkage and regrowth are explained by differing cancer and treatment: cancer type accounts for 66% of the variation in shrinkage rate and 71% of the variation in reported regrowth rates. Mean average parameter values by cancer and treatment are also reported., Conclusions: Mathematical modelling of longitudinal data is most often reported on a per clinical trial basis. However, the results reported here suggest that a more integrative approach would benefit the development of new treatments as well as the further optimisation of those currently used., Competing Interests: Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Purpose: Osimertinib is a potent and selective EGFR tyrosine kinase inhibitor (EGFR-TKI) of both sensitizing and T790M resistance mutations. To treat metastatic brain disease, blood-brain barrier (BBB) permeability is considered desirable for increasing clinical efficacy., Experimental Design: We examined the level of brain penetration for 16 irreversible and reversible EGFR-TKIs using multiple in vitro and in vivo BBB preclinical models., Results: In vitro osimertinib was the weakest substrate for human BBB efflux transporters (efflux ratio 3.2). In vivo rat free brain to free plasma ratios (Kpuu) show osimertinib has the most BBB penetrance (0.21), compared with the other TKIs (Kpuu ≤ 0.12). PET imaging in Cynomolgus macaques demonstrated osimertinib was the only TKI among those tested to achieve significant brain penetrance ( C max %ID 1.5, brain/blood Kp 2.6). Desorption electrospray ionization mass spectroscopy images of brains from mouse PC9 macrometastases models showed osimertinib readily distributes across both healthy brain and tumor tissue. Comparison of osimertinib with the poorly BBB penetrant afatinib in a mouse PC9 model of subclinical brain metastases showed only osimertinib has a significant effect on rate of brain tumor growth., Conclusions: These preclinical studies indicate that osimertinib can achieve significant exposure in the brain compared with the other EGFR-TKIs tested and supports the ongoing clinical evaluation of osimertinib for the treatment of EGFR-mutant brain metastasis. This work also demonstrates the link between low in vitro transporter efflux ratios and increased brain penetrance in vivo supporting the use of in vitro transporter assays as an early screen in drug discovery., (©2020 American Association for Cancer Research.)

We study a five-compartment mathematical model originally proposed by Kuznetsov et al. (1994) to investigate the effect of nonlinear interactions between tumour and immune cells in the tumour microenvironment, whereby immune cells may induce tumour cell death, and tumour cells may inactivate immune cells. Exploiting a separation of timescales in the model, we use the method of matched asymptotics to derive a new two-dimensional, long-timescale, approximation of the full model, which differs from the quasi-steady-state approximation introduced by Kuznetsov et al. (1994), but is validated against numerical solutions of the full model. Through a phase-plane analysis, we show that our reduced model is excitable, a feature not traditionally associated with tumour-immune dynamics. Through a systematic parameter sensitivity analysis, we demonstrate that excitability generates complex bifurcating dynamics in the model. These are consistent with a variety of clinically observed phenomena, and suggest that excitability may underpin tumour-immune interactions. The model exhibits the three stages of immunoediting - elimination, equilibrium, and escape, via stable steady states with different tumour cell concentrations. Such heterogeneity in tumour cell numbers can stem from variability in initial conditions and/or model parameters that control the properties of the immune system and its response to the tumour. We identify different biophysical parameter targets that could be manipulated with immunotherapy in order to control tumour size, and we find that preferred strategies may differ between patients depending on the strength of their immune systems, as determined by patient-specific values of associated model parameters., (Copyright © 2020. Published by Elsevier Ltd.)

Stability analysis, often overlooked in pharmacometrics, is essential to explore dynamical systems. The model developed by Friberg et al. 1 to describe drug-induced hematotoxicity is widely used to support decisions across drug development, and parameter values are often identified from observed blood counts. We use stability analysis to study the parametric dependence of stable and unstable solutions of several Friberg-type models and highlight the risks associated with system instability in the context of nonlinear mixed effects modeling. We emphasize the consequences of unstable solutions on prediction performance by demonstrating nonbiological system behaviors in a real case study of drug-induced thrombocytopenia. Ultimately, we provide simple criteria for identifying parameters associated with stable solutions of Friberg-type models. For instance, in the original Friberg model, we find that stability depends only on the parameter that governs the feedback from peripheral cells to progenitors and provide the exact range of values that results in stable solutions., (© 2020 AstraZeneca. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

Introduction: Osimertinib has shown promising activity in patients with leptomeningeal metastases (LMs) of EGFR-positive NSCLC at 160 mg once daily (qd) (BLOOM; NCT02228369). We report LM activity with osimertinib (80 mg qd) in a retrospective analysis of studies across the AURA program (AURA extension, AURA2, AURA17, and AURA3)., Methods: Patients with EGFR T790M-positive advanced NSCLC and progression after previous EGFR-tyrosine kinase inhibitor therapy received osimertinib (80 mg qd). Patients with central nervous system (CNS) metastases (including LMs) were eligible if the lesions were neurologically asymptomatic and stable. Patients with evidence of LMs at the study entry were retrospectively included for the analysis; brain scans were assessed for radiologic LM response by neuroradiologically blinded, independent central review per the modified Response Assessment in Neuro-Oncology LM criteria. LM objective response rate, duration of response, progression-free survival, and overall survival were assessed. A longitudinal analysis was performed to investigate the relationship between changes from the baseline in non-CNS tumor sizes and LM responses at each visit of patients in AURA LM and BLOOM studies., Results: For the 22 patients included in the analysis, LM objective response rate was 55% (95% confidence interval [CI]: 32-76). Median LM duration of response was not reached (95% CI: 2.8-not calculable [NC]). Median LM progression-free survival and overall survival were 11.1 months (95% CI: 4.6-NC) and 18.8 months (95% CI: 6.3-NC), respectively. The longitudinal analysis revealed similar non-CNS and LM responses between the patients in AURA LM and BLOOM programs., Conclusions: Patients with EGFR T790M-positive NSCLC and radiologically detected LM obtained clinical benefit from osimertinib (80 mg qd)., (Copyright © 2020 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Haematological toxicity associated with cancer therapeutics is monitored by changes in blood cell count, and their primary effect is on proliferative progenitors in the bone marrow. Using observations in rat bone marrow and blood, we characterize a mathematical model that comprises cell proliferation and differentiation of the full haematopoietic phylogeny, with interacting feedback loops between lineages in homeostasis as well as following carboplatin exposure. We accurately predicted the temporal dynamics of several mature cell types related to carboplatin-induced bone marrow toxicity and identified novel insights into haematopoiesis. Our model confirms a significant degree of plasticity within bone marrow cells, with the number and type of both early progenitors and circulating cells affecting cell balance, via feedback mechanisms, through fate decisions of the multipotent progenitors. We also demonstrated cross-species translation of our predictions to patients, applying the same core model structure and considering differences in drug-dependent and physiology-dependent parameters., (© 2019 Astrazeneca CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

AZD9496 ((E)-3-(3,5-difluoro-4-((1R,3R)-2-(2-fluoro-2-methylpropyl)-3-methyl-2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indol-1-yl)phenyl)acrylic acid) is an oral selective estrogen receptor degrader currently in clinical development for treatment of estrogen receptor-positive breast cancer. In a first-in-human phase 1 study, AZD9496 exhibited dose nonlinear pharmacokinetics, the mechanistic basis of which was investigated in this study. The metabolism kinetics of AZD9496 were studied using human liver microsomes (HLMs), recombinant cytochrome P450s (rP450s), and hepatocytes. In addition, modeling approaches were used to gain further mechanistic insights. CYP2C8 was predominantly responsible for biotransformation of AZD9496 to its two main metabolites whose rate of formation with increasing AZD9496 concentrations exhibited complete substrate inhibition in HLM, rCYP2C8, and hepatocytes. Total inhibition by AZD9496 of amodiaquine N -deethylation, a specific probe of CYP2C8 activity, confirmed the completeness of this inhibition. The commonly used substrate inhibition model analogous to uncompetitive inhibition fit poorly to the data. However, using the same model but without constraints on the number of molecules occupying the inhibitory binding site (i.e., n S 1 ES) provided a significantly better fit (F test, P < 0.005). With the improved model, up to three AZD9496 molecules were predicted to bind the inhibitory site of CYP2C8. In contrast to previous studies showing substrate inhibition of P450s to be partial, our results demonstrate complete substrate inhibition of CYP2C8 via binding of more than one molecule of AZD9496 to the inhibitory site. As CYP2C8 appears to be the sole isoform catalyzing formation of the main metabolites, the substrate inhibition might explain the observed dose nonlinearity in the clinic at higher doses., (Copyright © 2018 by The American Society for Pharmacology and Experimental Therapeutics.)

Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest cancers, and overall survival rates have barely improved over the past five decades. The antimetabolite gemcitabine remains part of the standard of care but shows very limited antitumor efficacy. Ataxia telangiectasia and Rad3-related protein (ATR), the apical kinase of the intra-S-phase DNA damage response, plays a central role in safeguarding cells from replication stress and can therefore limit the efficacy of antimetabolite drug therapies. We investigated the ability of the ATR inhibitor, AZD6738, to prevent the gemcitabine-induced intra-S-phase checkpoint activation and evaluated the antitumor potential of this combination in vitro and in vivo In PDAC cell lines, AZD6738 inhibited gemcitabine-induced Chk1 activation, prevented cell-cycle arrest, and restrained RRM2 accumulation, leading to the strong induction of replication stress markers only with the combination. Moreover, synergistic growth inhibition was identified in a panel of 5 mouse and 7 human PDAC cell lines using both Bliss Independence and Loewe models. In clonogenic assays, the combination abrogated survival at concentrations for which single agents had minor effects. In vivo , AZD6738 in combination with gemcitabine was well tolerated and induced tumor regression in a subcutaneous allograft model of a Kras G12D ; Trp53 R172H ; Pdx-Cre (KPC) mouse cancer cell line, significantly extending survival. Remarkably, the combination also induced regression of a subgroup of KPC autochthonous tumors, which generally do not respond well to conventional chemotherapy. Altogether, our data suggest that AZD6738 in combination with gemcitabine merits evaluation in a clinical trial in patients with PDAC. Mol Cancer Ther; 17(8); 1670-82. ©2018 AACR ., (©2018 American Association for Cancer Research.)

Objective: The minimal model is used to estimate insulin sensitivity in patients with diabetes, following an intravenous glucose tolerance test (IVGTT). Issues have been reported regarding parameter estimation, including correlation between insulin sensitivity and action parameters. The objective was to reduce these issues, by modifying the input of glucose in the test., Methods: Data were available for 24 volunteers following an IVGTT and glucose clamp test. Correlation between parameters was explored using likelihood heatmaps. An integrated glucose-insulin model was used to simulate glucose and insulin concentrations following new glucose inputs. The improved input for the test was selected by finding the minimum inverse of the determinant of the Fisher information matrix., Key Findings: When the minimal model was fitted to the IVGTT data, there was clear correlation between the insulin parameters. With the glucose clamp, all parameters were correlated and badly estimated. The modified input, a bolus dose followed by constant infusion, resulted in improvement in parameter estimation and reduction in parameter correlation., Conclusions: It is possible to reduce the issues with parameter estimation in the minimal model by modifying the glucose input, leading to a simplified test deign and a reduction in the total amount of glucose infused., (© 2017 The Authors. Journal of Pharmacy and Pharmacology published by John Wiley & Sons Ltd on behalf of Royal Pharmaceutical Society.)

Purpose: Approximately one-third of patients with non-small cell lung cancer (NSCLC) harboring tumors with EGFR-tyrosine kinase inhibitor (TKI)-sensitizing mutations (EGFRm) experience disease progression during treatment due to brain metastases. Despite anecdotal reports of EGFR-TKIs providing benefit in some patients with EGFRm NSCLC brain metastases, there is a clinical need for novel EGFR-TKIs with improved efficacy against brain lesions., Experimental Design: We performed preclinical assessments of brain penetration and activity of osimertinib (AZD9291), an oral, potent, irreversible EGFR-TKI selective for EGFRm and T790M resistance mutations, and other EGFR-TKIs in various animal models of EGFR-mutant NSCLC brain metastases. We also present case reports of previously treated patients with EGFRm-advanced NSCLC and brain metastases who received osimertinib in the phase I/II AURA study (NCT01802632)., Results: Osimertinib demonstrated greater penetration of the mouse blood-brain barrier than gefitinib, rociletinib (CO-1686), or afatinib, and at clinically relevant doses induced sustained tumor regression in an EGFRm PC9 mouse brain metastases model; rociletinib did not achieve tumor regression. Under positron emission tomography micro-dosing conditions, [ 11 C]osimertinib showed markedly greater exposure in the cynomolgus monkey brain than [ 11 C]rociletinib and [ 11 C]gefitinib. Early clinical evidence of osimertinib activity in previously treated patients with EGFRm-advanced NSCLC and brain metastases is also reported., Conclusions: Osimertinib may represent a clinically significant treatment option for patients with EGFRm NSCLC and brain metastases. Further investigation of osimertinib in this patient population is ongoing. Clin Cancer Res; 22(20); 5130-40. ©2016 AACR., (©2016 American Association for Cancer Research.)

Osimertinib (AZD9291) is a potent, selective, irreversible inhibitor of EGFR-sensitizing (exon 19 and L858R) and T790M-resistant mutation. In vivo, in the mouse, it is metabolized to an active des-methyl metabolite, AZ5104. To understand the therapeutic potential in patients, this study aimed to assess the relationship between osimertinib pharmacokinetics, the pharmacokinetics of the active metabolite, the pharmacodynamics of phosphorylated EGFR reduction, and efficacy in mouse xenograft models of EGFR-driven cancers, including two NSCLC lines. Osimertinib was dosed in xenografted models of EGFR-driven cancers. In one set of experiments, changes in phosphorylated EGFR were measured to confirm target engagement. In a second set of efficacy studies, the resulting changes in tumor volume over time after repeat dosing of osimertinib were observed. To account for the contributions of both molecules, a mathematical modeling approach was taken to integrate the resulting datasets. The model was able to describe the pharmacokinetics, pharmacodynamics, and efficacy in A431, PC9, and NCI-H1975 xenografts, with the differences in sensitivity described by the varying potency against wild-type, sensitizing, and T790M-mutant EGFR and the phosphorylated EGFR reduction required to reduce tumor volume. It was inferred that recovery of pEGFR is slower after chronic dosing due to reduced resynthesis. It was predicted and further demonstrated that although inhibition is irreversible, the resynthesis of EGFR is such that infrequent intermittent dosing is not as efficacious as once daily dosing. Mol Cancer Ther; 15(10); 2378-87. ©2016 AACR., (©2016 American Association for Cancer Research.)

mTOR is an atypical serine threonine kinase involved in regulating major cellular functions, such as nutrients sensing, growth, and proliferation. mTOR is part of the multiprotein complexes mTORC1 and mTORC2, which have been shown to play critical yet functionally distinct roles in the regulation of cellular processes. Current clinical mTOR inhibitors only inhibit the mTORC1 complex and are derivatives of the macrolide rapamycin (rapalogs). Encouraging effects have been observed with rapalogs in estrogen receptor-positive (ER(+)) breast cancer patients in combination with endocrine therapy, such as aromatase inhibitors. AZD2014 is a small-molecule ATP competitive inhibitor of mTOR that inhibits both mTORC1 and mTORC2 complexes and has a greater inhibitory function against mTORC1 than the clinically approved rapalogs. Here, we demonstrate that AZD2014 has broad antiproliferative effects across multiple cell lines, including ER(+) breast models with acquired resistance to hormonal therapy and cell lines with acquired resistance to rapalogs. In vivo, AZD2014 induces dose-dependent tumor growth inhibition in several xenograft and primary explant models. The antitumor activity of AZD2014 is associated with modulation of both mTORC1 and mTORC2 substrates, consistent with its mechanism of action. In combination with fulvestrant, AZD2014 induces tumor regressions when dosed continuously or using intermittent dosing schedules. The ability to dose AZD2014 intermittently, together with its ability to block signaling from both mTORC1 and mTORC2 complexes, makes this compound an ideal candidate for combining with endocrine therapies in the clinic. AZD2014 is currently in phase II clinical trials., (©2015 American Association for Cancer Research.)

It is challenging to evaluate how tumour pathophysiology influences nanomedicine therapeutic effect; however, this is a key question in drug delivery. An advanced analytical method was developed to quantify the spatial distribution of drug-induced effect in tumours with varied stromal morphologies. The analysis utilises standard immunohistochemistry images and quantifies the frequency of positive staining as a function of distance from the stroma. Two stromal morphologies - Estuary and Tumour Island - were classified in 28 tumours from a lung cancer explant model in mice treated with liposomal doxorubicin. Analysis demonstrated that Estuary-like tumours presented a highly convoluted tumour-stroma interface, with most tumour cells in close proximity to vessels; these tumours were 8.8-fold more responsive to liposomal doxorubicin than were Tumour Island-like tumours, which were nearly unresponsive to liposomal doxorubicin. SDARS analysis allows the relative treatment effect to be assessed in tumours individually, and enables investigation of nanomedicine delivery in complex tumour pathophysiologies., From the Clinical Editor: Advances in nanotechnology have brought about many novel treatment modalities for cancer. Nonetheless, there is no standard evaluation technique for tumor cells' drug response. The authors here utilized patient-derived tumour xenograft (PDTX) models to have a more translatable pre-clinical evaluation platform for nanomedicine drugs. They then used advanced imaging acquisition technique to analyze tumor stromal morphology, which they named Spatial Distribution of Apoptosis Relative to Stroma (SDARS). The findings would have significant clinical impact as it would help predict the eventual clinical drug response., (Copyright © 2015 Elsevier Inc. All rights reserved.)