Search

Therapeutic neutralization of Oncostatin M (OSM) causes mechanism‐driven anemia and thrombocytopenia, which narrows the therapeutic window complicating the selection of doses (and dosing intervals) that optimize efficacy and safety. We utilized clinical data from studies of an anti‐OSM monoclonal antibody (GSK2330811) in healthy volunteers (n = 49) and systemic sclerosis patients (n = 35), to quantitatively determine the link between OSM and alterations in red blood cell (RBC) and platelet production. Longitudinal changes in hematopoietic variables (including RBCs, reticulocytes, platelets, erythropoietin, and thrombopoietin) were linked in a physiology‐based model, to capture the long‐term effects and variability of therapeutic OSM neutralization on human hematopoiesis. Free serum OSM stimulated precursor cell production through sigmoidal relations, with higher maximum suppression (Imax) and OSM concentration for 50% suppression (IC50) for platelets (89.1% [95% confidence interval: 83.4–93.0], 6.03 pg/mL [4.41–8.26]) than RBCs (57.0% [49.7–64.0], 2.93 pg/mL [2.55–3.36]). Reduction in hemoglobin and platelets increased erythro‐ and thrombopoietin, respectively, prompting reticulocytosis and (partially) alleviating OSM‐restricted hematopoiesis. The physiology‐based model was substantiated by preclinical data and utilized in exploration of once‐weekly or every other week dosing regimens. Predictions revealed an (for the indication) unacceptable occurrence of grade 2 (67% [58–76], 29% [20–38]) and grade 3 (17% [10–25], 3% [0–7]) anemias, with limited thrombocytopenia. Individual extent of RBC precursor modulation was moderately correlated to skin mRNA gene expression changes. The physiological basis and consideration of interplay among hematopoietic variables makes the model generalizable to other drug and nondrug scenarios, with adaptations for patient populations, diseases, and therapeutics that modulate hematopoiesis or exhibit risk of anemia and/or thrombocytopenia. [ABSTRACT FROM AUTHOR]

The AIDA randomized clinical trial found no significant difference in clinical failure or survival between colistin monotherapy and colistin–meropenem combination therapy in carbapenem‐resistant Gram‐negative infections. The aim of this reverse translational study was to integrate all individual preclinical and clinical pharmacokinetic–pharmacodynamic (PKPD) data from the AIDA trial in a pharmacometric framework to explore whether individualized predictions of bacterial burden were associated with the trial outcomes. The compiled dataset included for each of the 207 patients was (i) information on the infecting Acinetobacter baumannii isolate (minimum inhibitory concentration, checkerboard assay data, and fitness in a murine model), (ii) colistin plasma concentrations and colistin and meropenem dosing history, and (iii) disease scores and demographics. The individual information was integrated into PKPD models, and the predicted change in bacterial count at 24 h for each patient, as well as patient characteristics, was correlated with clinical outcomes using logistic regression. The in vivo fitness was the most important factor for change in bacterial count. A model‐predicted growth at 24 h of ≥2‐log10 (164/207) correlated positively with clinical failure (adjusted odds ratio, aOR = 2.01). The aOR for one unit increase of other significant predictors were 1.24 for SOFA score, 1.19 for Charlson comorbidity index, and 1.01 for age. This study exemplifies how preclinical and clinical anti‐infective PKPD data can be integrated through pharmacodynamic modeling and identify patient‐ and pathogen‐specific factors related to clinical outcomes – an approach that may improve understanding of study outcomes. [ABSTRACT FROM AUTHOR]

Insight into the development of treatment resistance can support the optimization of anticancer treatments. This study aims to characterize the tumor dynamics and development of drug resistance in patients with non‐small cell lung cancer treated with erlotinib, and investigate the relationship between baseline circulating tumor DNA (ctDNA) data and tumor dynamics. Data obtained for the analysis included (1) intensively sampled erlotinib concentrations from 29 patients from two previous pharmacokinetic (PK) studies, and (2) tumor sizes, ctDNA measurements, and sparsely sampled erlotinib concentrations from 18 patients from the START‐TKI study. A two‐compartment population PK model was first developed which well‐described the PK data. The PK model was subsequently applied to investigate the exposure‐tumor dynamics relationship. To characterize the tumor dynamics, models accounting for intra‐tumor heterogeneity and acquired resistance with or without primary resistance were investigated. Eventually, the model assumed acquired resistance only resulted in an adequate fit. Additionally, models with or without exposure‐dependent treatment effect were explored, and no significant exposure‐response relationship for erlotinib was identified within the observed exposure range. Subsequently, the correlation of baseline ctDNA data on EGFR and TP53 variants with tumor dynamics' parameters was explored. The analysis indicated that higher baseline plasma EGFR mutation levels correlated with increased tumor growth rates, and the inclusion of ctDNA measurements improved model fit. This result suggests that quantitative ctDNA measurements at baseline have the potential to be a predictor of anticancer treatment response. The developed model can potentially be applied to design optimal treatment regimens that better overcome resistance. [ABSTRACT FROM AUTHOR]

Appropriate antibiotic dosing to ensure early and sufficient target attainment is crucial for improving clinical outcome in critically ill patients. Parametric survival analysis is a preferred modeling method to quantify time‐varying antibiotic exposure – response effects, whereas bias may be introduced in hazard functions and survival functions when competing events occur. This study investigated predictors of in‐hospital mortality in critically ill patients treated with meropenem by pharmacometric multistate modeling. A multistate model comprising five states (ongoing meropenem treatment, other antibiotic treatment, antibiotic treatment termination, discharge, and death) was developed to capture the transitions in a cohort of 577 critically ill patients treated with meropenem. Various factors were investigated as potential predictors of the transitions, including patient demographics, creatinine clearance calculated by Cockcroft–Gault equation (CLCRCG), time that unbound concentrations exceed the minimum inhibitory concentration (fT>MIC), and microbiology‐related measures. The probabilities to transit to other states from ongoing meropenem treatment increased over time. A 10 mL/min decrease in CLCRCG was found to elevate the hazard of transitioning from states of ongoing meropenem treatment and antibiotic treatment termination to the death state by 18%. The attainment of 100% fT>MIC significantly increased the transition rate from ongoing meropenem treatment to antibiotic treatment termination (by 9.7%), and was associated with improved survival outcome. The multistate model prospectively assessed predictors of death and can serve as a useful tool for survival analysis in different infection scenarios, particularly when competing risks are present. [ABSTRACT FROM AUTHOR]

Neutrophil granulocytes are key components of the host response against pathogens, and severe neutropenia, with neutrophil counts below 0.5 × 106 cells/mL, renders patients increasingly vulnerable to infections. Published in vitro (n = 7) and in vivo (n = 5) studies with time‐course information on bacterial and neutrophil counts were digitized to characterize the kinetics of neutrophil‐mediated bacterial killing and inform on the immune systems' contribution to the clearance of bacterial infections. A mathematical model for the in vitro dynamics of bacteria and the kinetics of neutrophil‐mediated phagocytosis and digestion was developed, which was extended to in vivo studies in immune‐competent and immune‐compromised mice. Neutrophil‐mediated bacterial killing was described by two first‐order processes—phagocytosis and digestion—scaled by neutrophil concentration, where 50% of the maximum was achieved at neutrophil counts of 1.19 × 106 cells/mL (phagocytosis) and 6.55 × 106 cells/mL (digestion). The process efficiencies diminished as the phagocytosed bacteria to total neutrophils ratio increased (with 50% reduction at a ratio of 3.41). Neutrophil in vivo dynamics were captured through the characterization of myelosuppressive drug effects and postinoculation neutrophil influx into lungs and by system differences (27% bacterial growth and 9.3% maximum capacity, compared with in vitro estimates). Predictions showed how the therapeutically induced reduction of neutrophil counts enabled bacterial growth, especially when falling below 0.5 × 106 cells/mL, whereas control individuals could deal with all tested bacterial burdens (up to 109 colony forming units/g lung). The model‐based characterization of neutrophil‐mediated bacterial killing simultaneously predicted data across in vitro and in vivo studies and may be used to inform the capacity of host–response at the individual level. [ABSTRACT FROM AUTHOR]

FAP‐4‐1BBL is a bispecific antibody exerting 4‐1BB‐associated T‐cell activation only while simultaneously bound to the fibroblast activation protein (FAP) receptor, expressed on the surface of cancer‐associated fibroblasts. The trimeric complex formed when FAP‐4‐1BBL is simultaneously bound to FAP and 4‐1BB represents a promising mechanism to achieve tumor‐specific 4‐1BB stimulation. We integrated in vitro data with mathematical modeling to characterize the pharmacology of FAP‐4‐1BBL as a function of trimeric complex formation when combined with the T‐cell engager cibisatamab. This relationship was used to prospectively predict a range of clinical doses where trimeric complex formation is expected to be at its maximum. Depending on the dosing schedule and FAP‐4‐1BBL plasma: tumor distribution, doses between 2 and 145 mg could lead to maximum trimeric complex formation in the clinic. Due to the expected variability in both pharmacokinetic and FAP expression in the patient population, we predict that detecting a clear dose–response relationship would remain difficult without a large number of patients per dose level, highlighting that mathematical modeling techniques based on in vitro data could aid dose selection. [ABSTRACT FROM AUTHOR]

The dose/exposure‐efficacy analyses are often conducted separately for oncology end points like best overall response, progression‐free survival (PFS) and overall survival (OS). Multistate models offer to bridge these dose‐end point relationships by describing transitions and transition times from enrollment to response, progression, and death, and evaluating transition‐specific dose effects. This study aims to apply the multistate pharmacometric modeling and simulation framework in a dose optimization setting of bintrafusp alfa, a fusion protein targeting TGF‐β and PD‐L1. A multistate model with six states (stable disease [SD], response, progression, unknown, dropout, and death) was developed to describe the totality of endpoints data (time to response, PFS, and OS) of 80 patients with non‐small cell lung cancer receiving 500 or 1200 mg of bintrafusp alfa. Besides dose, evaluated predictor of transitions include time, demographics, premedication, disease factors, individual clearance derived from a pharmacokinetic model, and tumor dynamic metrics observed or derived from tumor size model. We found that probabilities of progression and death upon progression decreased over time since enrollment. Patients with metastasis at baseline had a higher probability to progress than patients without metastasis had. Despite dose failed to be statistically significant for any individual transition, the combined effect quantified through a model with dose‐specific transition estimates was still informative. Simulations predicted a 69.2% probability of at least 1 month longer, and, 55.6% probability of at least 2‐months longer median OS from the 1200 mg compared to the 500 mg dose, supporting the selection of 1200 mg for future studies. [ABSTRACT FROM AUTHOR]

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

Overall survival is defined as the time since randomization into the clinical trial to event of death or censor (end of trial or follow‐up), and is considered to be the most reliable cancer end point. However, the introduction of second‐line treatment after disease progression could influence survival and be considered a confounding factor. The aim of the current study was to set up a multistate model framework, using data from the IMpower131 study, to investigate the influence of second‐line immunotherapies on overall survival analysis. The model adequately described the transitions between different states in patients with advanced squamous non‐small cell lung cancer treated with or without atezolizumab plus nab‐paclitaxel and carboplatin, and characterized the survival data. High PD‐L1 expression at baseline was associated with a decreased hazard of progression, while the presence of liver metastasis at baseline was indicative of a high risk of disease progression after initial response. The hazard of death after progression was lower for participants who had longer treatment response, i.e., longer time to progression. The simulations based on the final multistate model showed that the addition of atezolizumab to the nab‐paclitaxel and carboplatin regimen had significant improvement in the patients' survival (hazard ratio = 0.75, 95% prediction interval: 0.61–0.90 favoring the atezolizumab + nab‐paclitaxel and carboplatin arm). The developed modeling approach can be applied to other cancer types and therapies to provide a better understanding of efficacy of drug and characterizing different states, and investigate the benefit of primary therapy in survival while accounting for the switch to alternative treatment in the case of disease progression. [ABSTRACT FROM AUTHOR]

Dr. Reddy's Laboratories rituximab (DRL_RI; Dr. Reddy's Laboratories SA, Basel, Switzerland) is under development as a rituximab biosimilar. Study RI‐01‐002 (Clinical Trials Registry ‐ India/2012/11/003129), comparing DRL_RI to the reference medicinal product (RMP) MabThera® (Roche, Grenzach‐Wyhlen, Germany), demonstrated pharmacokinetic (PK) equivalence and showed comparable pharmacodynamic, efficacy, safety, and immunogenicity profiles. We used data from the same study to perform population PK and PK–pharmacodynamic analyses: first exploring possible factors influencing the PK similarity assessment between products and then performing simulations to investigate the impact of tumor size on rituximab PK. Nonlinear mixed‐effects models for PK, tumor size, tumor size–PK, and tumor response were developed independently. The final PK model included drug product as a dose‐scaling parameter and predicted a 6.75% higher dose reaching the system in RMP‐treated patients. However, when tumor size was included in the tumor size–PK model, the drug product effect was no longer observed. The model rather indicated that patients with larger tumor size have higher clearance. Further simulations confirmed that higher baseline tumor size is associated to slightly lower rituximab exposure. Tumor response, described by a continuous‐time Markov model, did not differ between drug products. Both had higher effects during the first 20 weeks of treatment. Also, the model described a subpopulation of nonresponders to treatment (42%) with faster transitions to a worse state. The different rituximab exposure initially detected between drug products (6.75%) was shown using PK/PK–pharmacodynamic analysis to be attributed to a tumor size imbalance between treatment groups. PK/PK–pharmacodynamic analyses may contribute to PK similarity assessments. [ABSTRACT FROM AUTHOR]

Blood pressure measurements form a critical component of adverse event monitoring for tyrosine kinase inhibitors, but might also serve as a biomarker for dose titrations. This study explored the impact of various sources of within-individual variation on blood pressure readings to improve measurement practices and evaluated the utility for individual-and population-level dose selection. A pharmacokinetic-pharmacodynamic modeling framework was created to describe circadian blood pressure changes, inter-and intra-day variability, changes from dipper to non-dipper profiles, and the relationship between drug exposure and blood pressure changes over time. The framework was used to quantitatively evaluate the influence of physiological and pharmacological aspects on blood pressure measurements, as well as to compare measurement techniques, including office-based, home-based, and ambulatory 24-h blood pressure readings. Circadian changes, as well as random intra-day and inter-day variability, were found to be the largest sources of within-individual variation in blood pressure. Office-based and ambulatory 24-h measurements gave rise to potential bias (>5 mmHg), which was mitigated by model-based estimations. Our findings suggest that 5-8 consecutive, home-based, measurements taken at a consistent time around noon, or alternatively within a limited time frame (e.g., 8.00 a.m. to 12.00 p.m. or 12.00 p.m. to 5.00 p.m.), will give rise to the most consistent blood pressure estimates. Blood pressure measurements likely do not represent a sufficiently accurate method for individual-level dose selection, but may be valuable for population-level dose identification. A user-friendly tool has been made available to allow for interactive blood pressure simulations and estimations for the investigated scenarios. [ABSTRACT FROM AUTHOR]

The tumor size ratio (TSR), time‐to‐tumor growth (TTG), and tumor growth rate (kG) are frequently suggested as model‐based predictors of overall survival (OS) for different types of tumors. When the tumor metrics are applied in forecasting of the outcome for individual patients at an early stage, the tumor data might be sparse resulting in imprecise prediction. This simulation study aimed to investigate how the tumor follow‐up data and estimation approaches influence the accuracy in the tumor size metrics and the predicted hazard of death for individual patients. Longitudinal tumor size and OS data were simulated using tumor growth inhibition and Weibull distribution models, respectively. Based on the model and increasing measurement durations, the accuracy (defined as 80–125% of the simulated "true" value) in individual metrics and hazard was computed. TSR week 6 (TSRw6) accuracy was adequate for 91% of the patients when tumor size was measured up to 12 weeks. For TTG and kG metrics, the highest accuracy observed was lower (43 and 77%, respectively) and occurred later (42 and 60 weeks, respectively). The simultaneous (joint) and sequential estimation approaches resulted in similar accuracies, however, in general, the sequential approach where individual tumor size parameters are fixed, demonstrated inferior estimation properties. The TSRw6 and the model‐predicted tumor time course (absolute or relative change) had better forecasting properties than TTG or kG. The population pharmacokinetic (PK) parameters and data approach performed similarly or better than the simultaneous approach and had a better accuracy in estimating individuals' hazard of death than the individual PK parameters method. [ABSTRACT FROM AUTHOR]

Astegolimab is a fully human immunoglobulin G2 monoclonal antibody that binds to the ST2 receptor and blocks the interleukin‐33 signaling. It was evaluated in patients with uncontrolled severe asthma in the phase 2b study (Zenyatta) at doses of 70, 210, and 490 mg subcutaneously every 4 weeks for 52 weeks. This work aimed to characterize astegolimab pharmacokinetics, identify influential covariates contributing to its interindividual variability, and make a descriptive assessment of the exposure‐response relationships. A population pharmacokinetic model was developed using data from 368 patients in the Zenyatta study. Predicted average steady‐state concentration was used in the subsequent exposure‐response analyses, which evaluated efficacy (asthma exacerbation rate) and biomarker end points including forced expiratory volume in 1 second, fraction exhaled nitric oxide, blood eosinophils, and soluble ST2. A 2‐compartment disposition model with first‐order elimination and first‐order absorption best described the astegolimab pharmacokinetics. The relative bioavailability for the 70‐mg dose was 15.3% lower. Baseline body weight, estimated glomerular filtration rate, and eosinophils were statistically correlated with pharmacokinetic parameters, but only body weight had a clinically meaningful influence on the steady‐state exposure (ratios exceeding 0.8‐1.25). The exposure‐response of efficacy and biomarkers were generally flat with a weak trend in favor of the highest dose/exposure. This study characterized astegolimab pharmacokinetics in patients with asthma and showed typical pharmacokinetic behavior as a monoclonal antibody–based drug. The exposure‐response analyses suggested the highest dose tested in the Zenyatta study (490 mg every 4 weeks) performed close to the maximum effect, and no additional response may be expected above it. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop a multistate model for overall survival (OS) analysis, based on parametric hazard functions and combined with an investigation of predictors derived from a longitudinal tumor size model on the transition hazards. Different states – stable disease, tumor response, progression, second‐line treatment, and death following docetaxel treatment initiation (stable state) in patients with HER2‐negative breast cancer (n = 183) were used in model building. Past changes in tumor size prospectively predicts the probability of state changes. The hazard of death after progression was lower for subjects who had longer treatment response (i.e., longer time‐to‐progression). Young age increased the probability of receiving second‐line treatment. The developed multistate model adequately described the transitions between different states and jointly the overall event and survival data. The multistate model allows for simultaneous estimation of transition rates along with their tumor model derived metrics. The metrics were evaluated in a prospective manner so not to cause immortal time bias. Investigation of predictors and characterization of the time to develop response, the duration of response, the progression‐free survival, and the OS can be performed in a single multistate modeling exercise. This modeling approach can be applied to other cancer types and therapies to provide a better understanding of efficacy of drug and characterizing different states, thereby facilitating early clinical interventions to improve anticancer therapy. [ABSTRACT FROM AUTHOR]

Information on individual lesion dynamics and organ location are often ignored in pharmacometric modeling analyses of tumor response. Typically, the sum of their longest diameters is utilized. Herein, a tumor growth inhibition model was developed for describing the individual lesion time‐course data from 183 patients with metastatic HER2‐negative breast cancer receiving docetaxel. The interindividual variability (IIV), interlesion variability (ILV), and interorgan variability of parameters describing the lesion time‐courses were evaluated. Additionally, a model describing the probability of new lesion appearance and a time‐to‐event model for overall survival (OS), were developed. Before treatment initiation, the lesions were largest in the soft tissues and smallest in the lungs, and associated with a significant IIV and ILV. The tumor growth rate was 2.6 times higher in the breasts and liver, compared with other metastatic sites. The docetaxel drug effect in the liver, breasts, and soft tissues was greater than or equal to 1.2 times higher compared with other organs. The time‐course of the largest lesion, the presence of at least 3 liver lesions, and the time since study enrollment, increased the probability of new lesion appearance. New lesion appearance, along with the time to growth and time‐course of the largest lesion at baseline, were identified as the best predictors of OS. This tumor modeling approach, incorporating individual lesion dynamics, provided a more complete understanding of heterogeneity in tumor growth and drug effect in different organs. Thus, there may be potential to tailor treatments based on lesion location, lesion size, and early lesion response to provide better clinical outcomes. [ABSTRACT FROM AUTHOR]

Therapeutic drug monitoring (TDM) and model‐informed precision dosing (MIPD) have evolved as important tools to inform rational dosing of antibiotics in individual patients with infections. In particular, critically ill patients display altered, highly variable pharmacokinetics and often suffer from infections caused by less susceptible bacteria. Consequently, TDM has been used to individualize dosing in this patient group for many years. More recently, there has been increasing research on the use of MIPD software to streamline the TDM process, which can increase the flexibility and precision of dose individualization but also requires adequate model validation and re‐evaluation of existing workflows. In parallel, new minimally invasive and noninvasive technologies such as microneedle‐based sensors are being developed, which—together with MIPD software—have the potential to revolutionize how patients are dosed with antibiotics. Nonetheless, carefully designed clinical trials to evaluate the benefit of TDM and MIPD approaches are still sparse, but are critically needed to justify the implementation of TDM and MIPD in clinical practice. The present review summarizes the clinical pharmacology of antibiotics, conventional TDM and MIPD approaches, and evidence of the value of TDM/MIPD for aminoglycosides, beta‐lactams, glycopeptides, and linezolid, for which precision dosing approaches have been recommended. [ABSTRACT FROM AUTHOR]

Model‐informed drug development (MIDD) has a long and rich history in infectious diseases. This review describes foundational principles of translational anti‐infective pharmacology, including choice of appropriate measures of exposure and pharmacodynamic (PD) measures, patient subpopulations, and drug‐drug interactions. Examples are presented for state‐of‐the‐art, empiric, mechanistic, interdisciplinary, and real‐world evidence MIDD applications in the development of antibacterials (review of minimum inhibitory concentration‐based models, mechanism‐based pharmacokinetic/PD (PK/PD) models, PK/PD models of resistance, and immune response), antifungals, antivirals, drugs for the treatment of global health infectious diseases, and medical countermeasures. The degree of adoption of MIDD practices across the infectious diseases field is also summarized. The future application of MIDD in infectious diseases will progress along two planes; "depth" and "breadth" of MIDD methods. "MIDD depth" refers to deeper incorporation of the specific pathogen biology and intrinsic and acquired‐resistance mechanisms; host factors, such as immunologic response and infection site, to enable deeper interrogation of pharmacological impact on pathogen clearance; clinical outcome and emergence of resistance from a pathogen; and patient and population perspective. In particular, improved early assessment of the emergence of resistance potential will become a greater focus in MIDD, as this is poorly mitigated by current development approaches. "MIDD breadth" refers to greater adoption of model‐centered approaches to anti‐infective development. Specifically, this means how various MIDD approaches and translational tools can be integrated or connected in a systematic way that supports decision making by key stakeholders (sponsors, regulators, and payers) across the entire development pathway. [ABSTRACT FROM AUTHOR]

The value of model‐based translation in drug discovery and development is now effectively being recognized in many disease areas and among various stakeholders. Such quantitative approaches are expected to facilitate the selection on which compound to prioritize for successful development, predict the human efficacious dose based on preclinical data with adequate precision, guide design, and de‐risk later development stages. The importance of time‐dependencies, which are typically species‐dependent due to different turnover rates of biological processes, is, however, often neglected. For bacterial infections, the choice of dosing regimen is typically relying on preclinical pharmacokinetic (PK) and pharmacodynamic (PD) data, because the bacterial load and disease severity, and consequently the PK/PD relationship, cannot be quantified well on clinical data, given the low‐information end points used. It is time to recognize the limitations of using time‐collapsed approaches for translation (i.e., methods where targets are based on summary measures of exposure and response). Models describing the full time‐course captures important quantitative information of drug distribution, bacterial growth, antibiotic killing, and resistance development, and can account for species‐differences in the PK profiles driving the killing. Furthermore, with a model‐based approach for translation, we can take a holistic approach in development of a joint model for in vitro, in vivo, and clinical data, as well as incorporating information on the contribution of the immune system. Such advancements are anticipated to facilitate rational decision making during various stages of drug development and in the optimization of treatment regimens for different groups of patients. [ABSTRACT FROM AUTHOR]

Apramycin represents a subclass of aminoglycoside antibiotics that has been shown to evade almost all mechanisms of clinically relevant aminoglycoside resistance. Model‐informed drug development may facilitate its transition from preclinical to clinical phase. This study explored the potential of pharmacokinetic/pharmacodynamic (PK/PD) modeling to maximize the use of in vitro time‐kill and in vivo preclinical data for prediction of a human efficacious dose (HED) for apramycin. PK model parameters of apramycin from four different species (mouse, rat, guinea pig, and dog) were allometrically scaled to humans. A semimechanistic PK/PD model was developed from the rich in vitro data on four Escherichia coli strains and subsequently the sparse in vivo efficacy data on the same strains were integrated. An efficacious human dose was predicted from the PK/PD model and compared with the classical PK/PD index methodology and the aminoglycoside dose similarity. One‐compartment models described the PK data and human values for clearance and volume of distribution were predicted to 7.07 L/hour and 26.8 L, respectively. The required fAUC/MIC (area under the unbound drug concentration‐time curve over MIC ratio) targets for stasis and 1‐log kill in the thigh model were 34.5 and 76.2, respectively. The developed PK/PD model predicted the efficacy data well with strain‐specific differences in susceptibility, maximum bacterial load, and resistance development. All three dose prediction approaches supported an apramycin daily dose of 30 mg/kg for a typical adult patient. The results indicate that the mechanistic PK/PD modeling approach can be suitable for HED prediction and serves to efficiently integrate all available efficacy data with potential to improve predictive capacity. [ABSTRACT FROM AUTHOR]

The large heterogeneity in response to immune checkpoint inhibitors is driving the exploration of predictive biomarkers to identify patients who will respond to such treatment. We extended our previously suggested modeling framework of atezolizumab pharmacokinetics, IL18, and tumor size (TS) dynamics, to also include overall survival (OS). Baseline and model‐derived variables were explored as predictors of OS in 88 patients with non‐small cell lung cancer treated with atezolizumab. To investigate the impact of follow‐up length on the inclusion of predictors of OS, four different censoring strategies were applied. The time‐course of TS change was the most significant predictor in all scenarios, whereas IL18 was not significant. Identified predictors of OS were similar regardless of censoring strategy, although OS was underpredicted when patients were censored 5 months after last dose. The study demonstrated that the tumor‐time course‐OS relationship could be identified based on early phase I data. [ABSTRACT FROM AUTHOR]

Aims: Cladribine tablets have shown significant efficacy for the treatment of relapsing multiple sclerosis, a chronic and debilitating immune‐mediated disorder. This study was conducted to examine acute and/or cumulative effects of cladribine tablets 10 mg (3.5 or 5.25 mg/kg cumulative dose over 2 years) on heart rate, AV conduction and cardiac repolarization in patients with relapsing–remitting multiple sclerosis (RRMS). Methods: CLARITY was a 96‐week, double‐blind, placebo‐controlled, multicentre trial which evaluated the safety and efficacy of cladribine tablets 3.5 and 5.25 mg/kg body weight in patients with RRMS. A total of 135 patients were included in the ECG substudy, providing a total of 1534 post‐dose ECGs. ECG data were collected 15 minutes pre‐dose and between 0.5 and 3 hours post‐dose at pre‐study evaluation, study Day 1 and Weeks 5, 9, 13, 48 and 52. Results: For cladribine tablets 3.5 mg/kg, the maximum change in placebo‐adjusted post‐dose QTcF vs. visit‐baseline (BL) was −0.42 ms (90% CI: −3.61–4.44) at Week 1 (acute effects), and 3.20 ms (90% CI: −0.08–6.33) for cladribine tablets 5.25 mg/kg. The greatest observed differences in post‐dose QTcF vs. study BL occurred at Week 48 for both the 3.5 and 5.25 mg/kg doses of cladribine tablets with 5.99 ms (90% CI: 0.53–11.44) and 8.74 ms (90% CI: 3.18–14.31), respectively. No significant changes were observed in T‐wave morphology in either treatment group. Conclusions: Cladribine tablets 3.5 mg/kg (approved dose in Europe/other regions) did not confer clinically meaningful effects on heart rate, AV conduction and ventricular repolarization. [ABSTRACT FROM AUTHOR]

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

Abstract: Colistin is a polymyxin antibiotic used to treat patients infected with multidrug‐resistant Gram‐negative bacteria (MDR‐GNB). The objective of this work was to develop a whole‐body physiologically based pharmacokinetic (WB‐PBPK) model to predict tissue distribution of colistin in rat. The distribution of a drug in a tissue is commonly characterized by its tissue‐to‐plasma partition coefficient, Kp. Colistin and its prodrug, colistin methanesulfonate (CMS) Kp priors, were measured experimentally from rat tissue homogenates or predicted in silico. The PK parameters of both compounds were estimated fitting in vivo their plasma concentration–time profiles from six rats receiving an i.v. bolus of CMS. The variability in the data was quantified by applying a nonlinear mixed effect (NLME) modelling approach. A WB‐PBPK model was developed assuming a well‐stirred and perfusion‐limited distribution in tissue compartments. Prior information on tissue distribution of colistin and CMS was investigated following three scenarios: Kp was estimated using in silico Kp priors (I) or Kp was estimated using experimental Kp priors (II) or Kp was fixed to the experimental values (III). The WB‐PBPK model best described colistin and CMS plasma concentration–time profiles in scenario II. Colistin‐predicted concentrations in kidneys in scenario II were higher than in other tissues, which was consistent with its large experimental Kp prior. This might be explained by a high affinity of colistin for renal parenchyma and active reabsorption into the proximal tubular cells. In contrast, renal accumulation of colistin was not predicted in scenario I. Colistin and CMS clearance estimates were in agreement with published values. The developed model suggests using experimental priors over in silico Kp priors for kidneys to provide a better prediction of colistin renal distribution. Such models might serve in drug development for interspecies scaling and investigate the impact of disease state on colistin disposition. [ABSTRACT FROM AUTHOR]

In 2012, a new journal was launched from the ASCPT family, CPT: Pharmacometrics and Systems Pharmacology (PSP) as both quantitative system pharmacology (QSP) and pharmacometrics were growing fields in pharmacology, drug development, and drug use. In this Perspective, the present editors and associate editors of PSP want to share their strategic vision of where these two fields, separately and together, should, would, or could be 10 years from now. [ABSTRACT FROM AUTHOR]

AIMS Early identification of patients with febrile neutropenia (FN) is desirable for initiation of preventive treatment, such as with antibiotics. In this study, the time courses of two inflammation biomarkers, interleukin (IL)-6 and C-reactive protein (CRP), following adjuvant chemotherapy of breast cancer, were characterized. The potential to predict development of FN by IL-6 and CRP, and other model-derived and clinical variables, was explored. METHODS The IL-6 and CRP time courses in cycles 1 and 4 of breast cancer treatment were described by turnover models where the probability for an elevated production following initiation of chemotherapy was estimated. Parametric time-to-event models were developed to describe FN occurrence to assess: (i) predictors available before chemotherapy is initiated; (ii) predictors available before FN occurs; and (iii) predictors available when FN occurs. RESULTS The IL-6 and CRP time courses were successfully characterized with peak IL-6 typically occurring 2 days prior to CRP peak. Of all evaluated variables the CRP time course was most closely associated with the occurrence of FN. Since the CRP peak typically occurred at the time of FN diagnosis it will, however, have limited value for identifying the need for preventive treatment. The time course of IL-6 was the predictor that could best forecast FN events. Of the variables available at baseline, age was the best, although in comparison a relatively weak, predictor. CONCLUSIONS The developed models add quantitative knowledge about IL-6 and CRP and their relationship to the development of FN. The study suggests that IL-6 may have potential as a clinical predictor of FN if monitored during myelosuppressive chemotherapy. [ABSTRACT FROM AUTHOR]

Aims The aims of this study were (i) to develop a modelling framework linking change in tumour size during treatment to survival probability in metastatic ovarian cancer; and (ii) to model the appearance of new lesions and investigate their relationship with survival and disease characteristics. Methods Data from a randomized Phase III clinical trial comparing carboplatin monotherapy to gemcitabine plus carboplatin combotherapy in 336 patients with metastatic ovarian cancer were used. A population model describing change in tumour size based on drug treatment information was established and its relationship with time to appearance of new lesions and survival were investigated with time to event models. Results The tumour size profiles were well characterized as evaluated by visual predictive checks. Metastasis in the liver at enrolment and change in tumour size up to week 12 were predictors of time to appearance of new lesions. Survival was predicted based on the patient tumour size and ECOG performance status at enrolment and on appearance of new lesions during treatment and change in tumour size up to week 12. Tumour size and survival data from a separate study were adequately predicted. Conclusions The proposed models simulate tumour dynamics following treatment and provide a link to the probability of developing new lesions as well as to survival. The models have potential to be used for optimizing the design of late phase clinical trials in metastatic ovarian cancer based on early phase clinical study results and simulation. [ABSTRACT FROM AUTHOR]

In oncology trials, overall survival ( OS) is considered the most reliable and preferred endpoint to evaluate the benefit of drug treatment. Other relevant variables are also collected from patients for a given drug and its indication, and it is important to characterize the dynamic effects and links between these variables in order to improve the speed and efficiency of clinical oncology drug development. However, the drug-induced effects and causal relationships are often difficult to interpret because of temporal differences. To address this, population pharmacokinetic-pharmacodynamic ( PKPD) modelling and parametric time-to-event ( TTE) models are becoming more frequently applied. Population PKPD and TTE models allow for exploration towards describing the data, understanding the disease and drug action over time, investigating relevance of biomarkers, quantifying patient variability and in designing successful trials. In addition, development of models characterizing both desired and adverse effects in a modelling framework support exploration of risk-benefit of different dosing schedules. In this review, we have summarized population PKPD modelling analyses describing tumour, tumour marker and biomarker responses, as well as adverse effects, from anticancer drug treatment data. Various model-based metrics used to drive PD response and predict OS for oncology drugs and their indications are also discussed. [ABSTRACT FROM AUTHOR]

Our professional and academic community lost a great pioneer in the field of pharmacokinetics and pharmacometrics when Dr. Hartmut Derendorf, distinguished professor at the University of Florida College of Pharmacy passed away unexpectedly on November 23, 2020, at the age of 67. A native of Germany, Dr. Derendorf received his BS and PhD in pharmacy from the University of Münster and then joined University of Florida (UF) as a postdoctoral fellow in 1981. Dr. Hartmut Derendorf, PhD, a world-renowned expert in pharmacokinetics and pharmacometrics (1953-2020). [Extracted from the article]

Aims Although genetic, clinical and demographic factors have been shown to explain approximately half of the inter-individual variability in warfarin dose requirement in adults, less is known about causes of dose variability in children. This study aimed to identify and quantify major genetic, clinical and demographic sources of warfarin dose variability in children using modelling and simulation. Methods Clinical, demographic and genetic data from 163 children with a median age of 6.3 years (range 0.06-18.9 years), covering over 183 years of warfarin therapy and 6445 INR observations were used to update and optimize a published adult pharmacometric warfarin model for use in children. Results Genotype effects in children were found to be comparable with what has been reported for adults, with CYP2C9 explaining up to a four-fold difference in dose ( CYP2C9 *1/*1 vs. *3/*3) and VKORC1 explaining up to a two-fold difference in dose ( VKORC1 G/G vs. A/A), respectively. The relationship between bodyweight and warfarin dose was non-linear, with a three-fold difference in dose for a four-fold difference in bodyweight. In addition, age, baseline and target INR, and time since initiation of therapy, but not CYP4F2 genotype, had a significant impact on typical warfarin dose requirements in children. Conclusions The updated model provides quantitative estimates of major clinical, demographic and genetic factors impacting on warfarin dose variability in children. With this new knowledge more individualized dosing regimens can be developed and prospectively evaluated in the pursuit of improving both efficacy and safety of warfarin therapy in children. [ABSTRACT FROM AUTHOR]

The article presents a study that compares the efficiency of the agonist-antagonist interaction (AAI) model and prolactin pool model for assessment of the effect of remoxipride on prolactin. The study assesses the two models in eight male subjects, who were infused with remoxipride with criteria such as goodness-of-fit (GOF) and objective function values (OFV). Result shows that pool model is better to use for circadian rhythm function, while AAI describes better circadian rhythm of prolactin.

One of the most employed approaches to reduce severe neutropenia following anticancer drug regimens is to reduce the consecutive dose in fixed steps, commonly by 25%. Another approach has been to use pharmacokinetic (PK) sampling to tailor dosing, but only rarely have model-based computer approaches utilizing collected PK and/or pharmacodynamic (PD) data been used. A semi-mechanistic model for myelosuppression that can characterize the interindividual and interoccasion variability in the time-course of neutrophils following administration of a wide range of anticancer drugs may be used in a clinical setting for model-based dose individualization. The aim of this study was to compare current stepwise procedures to model-based dose adaptation by simulations, and investigate if the overall dose intensity in the population could be increased without increasing the risk of severe toxicity. The value of various amounts of PK- and/or PD-information was compared to standard dosing strategies using a maximum a posteriori procedure in NONMEM. The results showed that when information on neutrophil counts was available, the additional improvement from PK sampling was negligible. Using neutrophil sampling at baseline and an observation near the predicted nadir increased the number of patients in the target range by 27% in comparison with a one-sided 25% dose adjustment schedule, while keeping the number of patients experiencing severe toxicity at a comparable low level after five courses of treatment. High interindividual variability did not limit the benefit of model-based dose adaptation, whereas high interoccasion variability was predicted to make any dose adaptation method less successful. This study indicates that for successful model-based dose adaptation clinically, there is no need for drug concentration sampling, and that one extra neutrophil measurement in addition to the pre-treatment value is sufficient to limit severe neutropenia while increasing dose intensity. [ABSTRACT FROM AUTHOR]

Aims To develop a pharmacokinetic–pharmacodynamic model describing the time-course of QT interval prolongation after citalopram overdose and to evaluate the effect of charcoal on the relative risk of developing abnormal QT and heart-rate combinations. Methods Plasma concentrations and electrocardiograph (ECG) data from 52 patients after 62 citalopram overdose events were analysed in WinBUGS using a Bayesian approach. The reported doses ranged from 20 to 1700 mg and on 17 of the events a single dose of activated charcoal was administered. The developed pharmacokinetic–pharmacodynamic model was used for predicting the probability of having abnormal combinations of QT-RR, which was assumed to be related to an increased risk for torsade de pointes (TdP). Results The absolute QT interval was related to the observed heart rate with an estimated individual heart-rate correction factor [α = 0.36, between-subject coefficient of variation (CV) = 29%]. The heart-rate corrected QT interval was linearly dependent on the predicted citalopram concentration (slope = 40 ms l mg−1, between-subject CV = 70%) in a hypothetical effect-compartment (half-life of effect-delay = 1.4 h). The heart-rate corrected QT was predicted to be higher in women than in men and to increase with age. Administration of activated charcoal resulted in a pronounced reduction of the QT prolongation and was shown to reduce the risk of having abnormal combinations of QT-RR by approximately 60% for citalopram doses above 600 mg. Conclusion Citalopram caused a delayed lengthening of the QT interval. Administration of activated charcoal was shown to reduce the risk that the QT interval exceeds a previously defined threshold and therefore is expected to reduce the risk of TdP. [ABSTRACT FROM AUTHOR]

For many oncological agents, myelosuppression is the dose-limiting toxicity and the quantitative characterisation of the relationship between drug dose, plasma concentration and haematological toxicity is of importance in the drug development. Mechanism-based population pharmacokinetic-pharmacodynamic models have been developed for this purpose and the applications of these in candidate selection, first-in-man studies, prodrug and formulation development, dose finding, schedule optimisation, assessing influence of modifying agents, drug combination studies, subgroup identification and feedback individualisation are reviewed. [ABSTRACT FROM AUTHOR]