Combining mathematical modeling, in vitro data and clinical target expression to support bispecific antibody binding affinity selection: a case example with FAP-4-1BBL.

The majority of bispecific costimulatory antibodies in cancer immunotherapy are capable of exerting tumor-specific T-cell activation by simultaneously engaging both tumor-associated targets and costimulatory receptors expressed by T cells. The amount of trimeric complex formed when the bispecific antibody is bound simultaneously to the T cell receptor and the tumor-associated target follows a bell-shaped curve with increasing bispecific antibody exposure/dose. The shape of the curve is determined by the binding affinities of the bispecific antibody to its two targets and target expression. Here, using the case example of FAP-4-1BBL, a fibroblast activation protein alpha (FAP)-directed 4-1BB (CD137) costimulator, the impact of FAP-binding affinity on trimeric complex formation and pharmacology was explored using mathematical modeling and simulation. We quantified (1) the minimum number of target receptors per cell required to achieve pharmacological effect, (2) the expected coverage of the patient population for 19 different solid tumor indications, and (3) the range of pharmacologically active exposures as a function of FAP-binding affinity. A 10-fold increase in FAP-binding affinity (from a dissociation constant [KD] of 0.7 nM-0.07 nM) was predicted to reduce the number of FAP receptors needed to achieve 90% of the maximum pharmacological effect from 13,400 to 4,000. Also, the number of patients with colon cancer that would achieve 90% of the maximum effect would increase from 6% to 39%. In this work, a workflow to select binding affinities for bispecific antibodies that integrates preclinical in vitro data, mathematical modeling and simulation, and knowledge on target expression in the patient population, is provided. The early implementation of this approach can increase the probability of success with cancer immunotherapy in clinical development.

Therapeutic drug monitoring (TDM) of ß-lactam/ß-lactamase inhibitor (BL/BLI) drug combinations: insights from a pharmacometric simulation study.

BACKGROUND: The emergence of ß-lactamase-producing bacteria has led to the use of ß-lactam (BL) antibiotic and ß-lactamase inhibitor (BLI) drug combinations. Despite therapeutic drug monitoring (TDM) being endorsed for BLs, the impact of TDM on BLIs remains unclear. OBJECTIVES: Evaluate whether BLIs are available in effective exposures at the site of infection and assess if TDM of BLIs could be of interest. METHODS: Population pharmacokinetic models for 9 BL and BLI compounds were used to simulate drug concentrations at infection sites following EMA-approved dose regimens, considering plasma protein binding and tissue penetration. Predicted target site concentrations were used for probability of target attainment (PTA) analysis. RESULTS: Using EUCAST targets, satisfactory (≥90%) PTA was observed for BLs in patients with typical renal clearance (CrCL of 80 mL/min) across various sites of infection. However, results varied for BLIs. Avibactam achieved satisfactory PTA only in plasma, with reduced PTAs in abdomen (78%), lung (73%) and prostate (23%). Similarly, tazobactam resulted in unsatisfactory PTAs in intra-abdominal infections (79%), urinary tract infections (64%) and prostatitis (34%). Imipenem-relebactam and meropenem-vaborbactam achieved overall satisfactory PTAs, except in prostatitis and high-MIC infections for the latter combination. CONCLUSIONS: This study highlights the risk of solely relying on TDM of BLs, as this can indicate acceptable exposures of the BL while the BLI concentration, and consequently the combination, can result in suboptimal performance in terms of bacterial killing. Thus, dose adjustments also based on plasma concentration measurements of BLIs, in particular for avibactam and tazobactam, can be valuable in clinical practice to obtain effective exposures at the target site.

Model-based translation of results from in vitro to in vivo experiments for afabicin activity against Staphylococcus aureus.

BACKGROUND: Translation of experimental data on antibiotic activity typically relies on pharmacokinetic/pharmacodynamic (PK/PD) indices. Model-based approaches, considering the full antibiotic killing time course, could be an alternative. OBJECTIVES: To develop a mechanism-based modelling framework to assess the in vitro and in vivo activity of the FabI inhibitor antibiotic afabicin, and explore the ability of a model built on in vitro data to predict in vivo outcome. METHODS: A PK/PD model was built to describe bacterial counts from 162 static in vitro time-kill curves evaluating the effect of afabicin desphosphono, the active moiety of the prodrug afabicin, against 21 Staphylococcus aureus strains. Combined with a mouse PK model, outcomes of afabicin doses of 0.011-190 mg/kg q6h against nine S. aureus strains in a murine thigh infection model were predicted, and thereafter refined by estimating PD parameters. RESULTS: A sigmoid Emax model, with EC50 scaled by the MIC described the afabicin desphosphono killing in vitro. This model predicted, without parameter re-estimation, the in vivo bacterial counts at 24 h within a ±1 log margin for most dosing groups. When parameters were allowed to be estimated, EC50 was 38%-45% lower in vivo, compared with in vitro, within the studied MIC range. CONCLUSIONS: The developed PK/PD model described the time course of afabicin activity across experimental conditions and bacterial strains. This model showed translational capacity as parameters estimated on in vitro time-kill data could well predict the in vivo outcome for a wide variety of doses in a mouse thigh infection model.

Comparative Analysis of Traditional and Pharmacometric-Based Pharmacoeconomic Modeling in the Cost-Utility Evaluation of Sunitinib Therapy.

BACKGROUND: Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST). METHODS: A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario. RESULTS: The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16). CONCLUSIONS: Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address.

Physiologically-based pharmacokinetic modelling in sepsis; a tool to elucidate how pathophysiology affects meropenem pharmacokinetics: A PBPK model of meropenem in sepsis.

Applying physiologically-based pharmacokinetic (PBPK) modelling in sepsis could help to better understand how PK changes are influenced by drug- and patient-related factors. We aimed to elucidate the influence of sepsis pathophysiology on the PK of meropenem by applying PBPK modelling. A whole-body meropenem PBPK model was developed and evaluated in healthy individuals, and renally impaired non-septic patients. Sepsis-induced physiological changes in body composition, organ blood flow, kidney function, albumin, and haematocrit were implemented according to a previously proposed PBPK sepsis model. Model performance was evaluated, and a local sensitivity analysis was conducted. The model-predicted PK metrics (AUC, Cmax, CL, Vss) were within 1.33-fold-error margin of published data for 87.5% of the simulated profiles in healthy individuals. In sepsis, the model provided good predictions for literature-digitised average plasma and tissue exposure data, where the model-predicted AUC was within 1.33-fold-error margin for 9 out 11 simulated study profiles. Furthermore, the model was applied to individual plasma concentration data from 52 septic patients, where the model-predicted AUC, Cmax, and CL had a fold-error ratio range of 0.98-1.12, with alignment of the predicted and observed variability. For Vss, the fold-error ratio was 0.81, and the model underpredicted the population variability. CL was sensitive to renal plasma clearance, and kidney volume, whereas Vss was sensitive to the unbound fraction, organ volume fraction of the interstitial compartment, and the organ volume. These findings may be extended to more diverse drug types and support a more mechanistic understanding of the effect of sepsis on drug exposure.

Integration of individual preclinical and clinical anti-infective PKPD data to predict clinical study outcomes.

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.

Pharmacogenetic Testing or Therapeutic Drug Monitoring: A Quantitative Framework.

BACKGROUND: Pharmacogenetic profiling and therapeutic drug monitoring (TDM) have both been proposed to manage inter-individual variability (IIV) in drug exposure. However, determining the most effective approach for estimating exposure for a particular drug remains a challenge. This study aimed to quantitatively assess the circumstances in which pharmacogenetic profiling may outperform TDM in estimating drug exposure, under three sources of variability (IIV, inter-occasion variability [IOV], and residual unexplained variability [RUV]). METHODS: Pharmacokinetic models were selected from the literature corresponding to drugs for which pharmacogenetic profiling and TDM are both clinically considered approaches for dose individualization. The models were used to simulate relevant drug exposures (trough concentration or area under the curve [AUC]) under varying degrees of IIV, IOV, and RUV. RESULTS: Six drug cases were selected from the literature. Model-based simulations demonstrated that the percentage of patients for whom pharmacogenetic exposure prediction is superior to TDM differs for each drug case: tacrolimus (11.0%), tamoxifen (12.7%), efavirenz (49.2%), vincristine (49.6%), risperidone (48.1%), and 5-fluorouracil (5-FU) (100%). Generally, in the presence of higher unexplained IIV in combination with lower RUV and IOV, exposure was best estimated by TDM, whereas, under lower unexplained IIV in combination with higher IOV or RUV, pharmacogenetic profiling was preferred. CONCLUSIONS: For the drugs with relatively low RUV and IOV (e.g., tamoxifen and tacrolimus), TDM estimated true exposure the best. Conversely, for drugs with similar or lower unexplained IIV (e.g., efavirenz or 5-FU, respectively) combined with relatively high RUV, pharmacogenetic profiling provided the most accurate estimate for most patients. However, genotype prevalence and the relative influence of genotypes on the PK, as well as the ability of TDM to accurately estimate AUC with a limited number of samples, had an impact. The results could be used to support clinical decision making when considering other factors, such as the probability for severe side effects.

Impact of continuous-infusion meropenem degradation and infusion bag changes on bacterial killing of Pseudomonas aeruginosa based on model-informed translation.

BACKGROUND: Continuous infusion of meropenem has been proposed to increase target attainment in critically ill patients, although stability might limit its practical use. This study investigated the impact of meropenem degradation and infusion bag changes on the concentration-time profiles and bacterial growth and killing of P. aeruginosa given different continuous-infusion solutions. METHODS: A semi-mechanistic pharmacokinetic-pharmacodynamic (PK-PD) model quantifying meropenem concentrations (CMEM) and bacterial counts of a resistant P. aeruginosa strain (ARU552, MIC = 16 mg/L) over 24 h was used to translate in vitro antibiotic effects to patients with severe infections. Concentration-dependent drug degradation of saline infusion solutions was considered using an additional compartment in the population PK model. CMEM, fT>MIC (time that concentrations exceed the MIC) and total bacterial load (BTOT) after 24 h were simulated for different scenarios (n = 144), considering low- and high-dose regimens (3000/6000 mg/day±loading dose), clinically relevant infusion solutions (20/40/50 mg/mL), different intervals of infusion bag changes (every 8/24 h, q8/24 h), and varied renal function (creatinine clearance 40/80/120 mL/min) and MIC values (8/16 mg/L). RESULTS: Highest deviations between changing infusion bags q8h and q24h were observed for 50 mg/mL solutions and scenarios with CMEM_24h close to the MIC, with differences (Δ) in CMEM_24h up to 4.9 mg/L, ΔfT>MIC≤65.7%, and ΔBTOT_24h≤1.1 log10 CFU/mL, thus affecting conclusions on whether bacteriostasis was reached. CONCLUSIONS: In summary, this study indicated that for continuous infusion of meropenem, eight-hourly infusion bag changes improved PK/PD target attainment and might be beneficial particularly for high meropenem concentrations of saline infusion solutions and for plasma concentrations in close proximity to the MIC.

Physiologically-based pharmacokinetic models versus allometric scaling for prediction of tyrosine-kinase inhibitor exposure from adults to children.

PURPOSE: Model-based methods can predict pediatric exposure and support initial dose selection. The aim of this study was to evaluate the performance of allometric scaling of population pharmacokinetic (popPK) versus physiologically based pharmacokinetic (PBPK) models in predicting the exposure of tyrosine kinase inhibitors (TKIs) for pediatric patients (≥ 2 years), based on adult data. The drugs imatinib, sunitinib and pazopanib were selected as case studies due to their complex PK profiles including high inter-patient variability, active metabolites, time-varying clearances and non-linear absorption. METHODS: Pediatric concentration measurements and adult popPK models were derived from the literature. Adult PBPK models were generated in PK-Sim® using available physicochemical properties, calibrated to adult data when needed. PBPK and popPK models for the pediatric populations were translated from the models for adults and were used to simulate concentration-time profiles that were compared to the observed values. RESULTS: Ten pediatric datasets were collected from the literature. While both types of models captured the concentration-time profiles of imatinib, its active metabolite, sunitinib and pazopanib, the PBPK models underestimated sunitinib metabolite concentrations. In contrast, allometrically scaled popPK simulations accurately predicted all concentration-time profiles. Trough concentration (Ctrough) predictions from the popPK model fell within a 2-fold range for all compounds, while 3 out of 5 PBPK predictions exceeded this range for the imatinib and sunitinib metabolite concentrations. CONCLUSION: Based on the identified case studies it appears that allometric scaling of popPK models is better suited to predict exposure of TKIs in pediatric patients ≥ 2 years. This advantage may be attributed to the stable enzyme expression patterns from 2 years old onwards, which can be easily related to adult levels through allometric scaling. In some instances, both methods performed comparably. Understanding where discrepancies between the model methods arise, can further inform model development and ultimately support pediatric dose selection.

Quantifying combined effects of colistin and ciprofloxacin against Escherichia coli in an in silico pharmacokinetic-pharmacodynamic model.

Co-administering a low dose of colistin (CST) with ciprofloxacin (CIP) may improve the antibacterial effect against resistant Escherichia coli, offering an acceptable benefit-risk balance. This study aimed to quantify the interaction between ciprofloxacin and colistin in an in silico pharmacokinetic-pharmacodynamic model from in vitro static time-kill experiments (using strains with minimum inhibitory concentrations, MICCIP 0.023-1 mg/L and MICCST 0.5-0.75 mg/L). It was also sought to demonstrate an approach of simulating concentrations at the site of infection with population pharmacokinetic and whole-body physiologically based pharmacokinetic models to explore the clinical value of the combination when facing more resistant strains (using extrapolated strains with lower susceptibility). The combined effect in the final model was described as the sum of individual drug effects with a change in drug potency: for ciprofloxacin, concentration at half maximum killing rate (EC50) in combination was 160% of the EC50 in monodrug experiments, while for colistin, the change in EC50 was strain-dependent from 54.1% to 119%. The benefit of co-administrating a lower-than-commonly-administrated colistin dose with ciprofloxacin in terms of drug effect in comparison to either monotherapy was predicted in simulated bloodstream infections and pyelonephritis. The study illustrates the value of pharmacokinetic-pharmacodynamic modelling and simulation in streamlining rational development of antibiotic combinations.

Predicting the Long-Term Effects of Therapeutic Neutralization of Oncostatin M on Human Hematopoiesis.

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.

Quantitative modeling of tumor dynamics and development of drug resistance in non-small cell lung cancer patients treated with erlotinib.

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.

Increase in peg-asparaginase clearance as a predictor for inactivation in patients with acute lymphoblastic leukemia.

Asparaginase is an essential component of acute lymphoblastic leukemia (ALL) therapy, yet its associated toxicities often lead to treatment discontinuation, increasing the risk of relapse. Hypersensitivity reactions include clinical allergies, silent inactivation, or allergy-like responses. We hypothesized that even moderate increases in asparaginase clearance are related to later inactivation. We therefore explored mandatory monitoring of asparaginase enzyme activity (AEA) in patients with ALL aged 1-45 years treated according to the ALLTogether pilot protocol in the Nordic and Baltic countries to relate mean AEA to inactivation, to build a pharmacokinetic model to better characterize the pharmacokinetics of peg-asparaginase and assess whether an increased clearance relates to subsequent inactivation. The study analyzed 1631 real-time AEA samples from 253 patients, identifying inactivation in 18.2% of the patients. This inactivation presented as mild allergy (28.3%), severe allergy (50.0%), or silent inactivation (21.7%). A pharmacokinetic transit compartment model was used to describe AEA-time profiles, revealing that 93% of patients with inactivation exhibited prior increased clearance, whereas 86% of patients without hypersensitivity maintained stable clearance throughout asparaginase treatment. These findings enable prediction of inactivation and options for either dose increments or a shift to alternative asparaginase formulations to optimize ALL treatment strategies.

Multistate modeling for survival analysis in critically ill patients treated with meropenem.

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.

Bridging responses to a human telomerase reverse transcriptase-based peptide cancer vaccine candidate in a mechanism-based model.

Therapeutic cancer vaccines are novel immuno-therapeutics, aiming to improve clinical outcomes with other immunotherapies. However, obstacles to their successful clinical development remain, which model-informed drug development approaches may address. UV1 is a telomerase based therapeutic cancer vaccine candidate being investigated in phase I clinical trials for multiple indications. We developed a mechanism-based model structure, using a nonlinear mixed-effects modeling techniques, based on longitudinal tumor sizes (sum of the longest diameters, SLD), UV1-specific immunological assessment (stimulation index, SI) and overall survival (OS) data obtained from a UV1 phase I trial including non-small cell lung cancer (NSCLC) patients and a phase I/IIa trial including malignant melanoma (MM) patients. The final structure comprised a mechanistic tumor growth dynamics (TGD) model, a model describing the probability of observing a UV1-specific immune response (SI ≥ 3) and a time-to-event model for OS. The mechanistic TGD model accounted for the interplay between the vaccine peptides, immune system and tumor. The model-predicted UV1-specific effector CD4+ T cells induced tumor shrinkage with half-lives of 103 and 154 days in NSCLC and MM patients, respectively. The probability of observing a UV1-specific immune response was mainly driven by the model-predicted UV1-specific effector and memory CD4+ T cells. A high baseline SLD and a high relative increase from nadir were identified as main predictors for a reduced OS in NSCLC and MM patients, respectively. Our model predictions highlighted that additional maintenance doses, i.e. UV1 administration for longer periods, may result in more sustained tumor size shrinkage.

Synergy of polymyxin B and minocycline against KPC-3- and OXA-48-producing Klebsiella pneumoniae in dynamic time-kill experiments: agreement with in silico predictions.

OBJECTIVES: Combination therapy is often used for carbapenem-resistant Gram-negative bacteria. We previously demonstrated synergy of polymyxin B and minocycline against carbapenem-resistant Klebsiella pneumoniae in static time-kill experiments and developed an in silico pharmacokinetic/pharmacodynamic (PK/PD) model. The present study assessed the synergistic potential of this antibiotic combination in dynamic experiments. METHODS: Two clinical K. pneumoniae isolates producing KPC-3 and OXA-48 (polymyxin B MICs 0.5 and 8 mg/L, and minocycline MICs 1 and 8 mg/L, respectively) were included. Activities of the single drugs and the combination were assessed in 72 h dynamic time-kill experiments mimicking patient pharmacokinetics. Population analysis was performed every 12 h using plates containing antibiotics at 4× and 8× MIC. WGS was applied to reveal resistance genes and mutations. RESULTS: The combination showed synergistic and bactericidal effects against the KPC-3-producing strain from 12 h onwards. Subpopulations with decreased susceptibility to polymyxin B were frequently detected after single-drug exposures but not with the combination. Against the OXA-48-producing strain, synergy was observed between 4 and 8 h and was followed by regrowth. Subpopulations with decreased susceptibility to polymyxin B and minocycline were detected throughout experiments. For both strains, the observed antibacterial activities showed overall agreement with the in silico predictions. CONCLUSIONS: Polymyxin B and minocycline in combination showed synergistic effects, mainly against the KPC-3-producing K. pneumoniae. The agreement between the experimental results and in silico predictions supports the use of PK/PD models based on static time-kill data to predict the activity of antibiotic combinations at dynamic drug concentrations.

Model-Informed Precision Dosing to Reduce Vincristine-Induced Peripheral Neuropathy in Pediatric Patients: A Pharmacokinetic and Pharmacodynamic Modeling and Simulation Analysis.

BACKGROUND: Vincristine-induced peripheral neuropathy (VIPN) is a common adverse effect of vincristine, a drug often used in pediatric oncology. Previous studies demonstrated large inter- and intrapatient variability in vincristine pharmacokinetics (PK). Model-informed precision dosing (MIPD) can be applied to calculate patient exposure and individualize dosing using therapeutic drug monitoring (TDM) measurements. This study set out to investigate the PK/pharmacodynamic (PKPD) relationship of VIPN and determine the utility of MIPD to support clinical decisions regarding dose selection and individualization. METHODS: Data from 35 pediatric patients were utilized to quantify the relationship between vincristine dose, exposure and the development of VIPN. Measurements of vincristine exposure and VIPN (Common Terminology Criteria for Adverse Events [CTCAE]) were available at baseline and for each subsequent dosing occasions (1-5). A PK and PKPD analysis was performed to assess the inter- and intraindividual variability in vincristine exposure and VIPN over time. In silico trials were performed to portray the utility of vincristine MIPD in pediatric subpopulations with a certain age, weight and cytochrome P450 (CYP) 3A5 genotype distribution. RESULTS: A two-compartmental model with linear PK provided a good description of the vincristine exposure data. Clearance and distribution parameters were related to bodyweight through allometric scaling. A proportional odds model with Markovian elements described the incidence of Grades 0, 1 and ≥ 2 VIPN overdosing occasions. Vincristine area under the curve (AUC) was the most significant exposure metric related to the development of VIPN, where an AUC of 50 ng⋅h/mL was estimated to be related to an average VIPN probability of 40% over five dosing occasions. The incidence of Grade ≥ 2 VIPN reduced from 62.1 to 53.9% for MIPD-based dosing compared with body surface area (BSA)-based dosing in patients. Dose decreases occurred in 81.4% of patients with MIPD (vs. 86.4% for standard dosing) and dose increments were performed in 33.4% of patients (no dose increments allowed for standard dosing). CONCLUSIONS: The PK and PKPD analysis supports the use of MIPD to guide clinical dose decisions and reduce the incidence of VIPN. The current work can be used to support decisions with respect to dose selection and dose individualization in children receiving vincristine.

A model-based approach leveraging in vitro data to support dose selection from the outset: A framework for bispecific antibodies in immuno-oncology.

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.

Model-based assessment of neutrophil-mediated phagocytosis and digestion of bacteria across in vitro and in vivo studies.

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.

Predicting cytokine kinetics during sepsis; a modelling framework from a porcine sepsis model with live Escherichia coli.

BACKGROUND: Describing the kinetics of cytokines involved as biomarkers of sepsis progression could help to optimise interventions in septic patients. This work aimed to quantitively characterise the cytokine kinetics upon exposure to live E. coli by developing an in silico model, and to explore predicted cytokine kinetics at different bacterial exposure scenarios. METHODS: Data from published in vivo studies using a porcine sepsis model were analysed. A model describing the time courses of bacterial dynamics, endotoxin (ETX) release, and the kinetics of TNF and IL-6 was developed. The model structure was extended from a published model that quantifies the ETX-cytokines relationship. An external model evaluation was conducted by applying the model to literature data. Model simulations were performed to explore the sensitivity of the host response towards differences in the input rate of bacteria, while keeping the total bacterial burden constant. RESULTS: The analysis included 645 observations from 30 animals. The blood bacterial count was well described by a one-compartment model with linear elimination. A scaling factor was estimated to quantify the ETX release by bacteria. The model successfully described the profiles of TNF, and IL-6 without a need to modify the ETX-cytokines model structure. The kinetics of TNF, and IL-6 in the external datasets were well predicted. According to the simulations, the ETX tolerance development results in that low initial input rates of bacteria trigger the lowest cytokine release. CONCLUSION: The model quantitively described and predicted the cytokine kinetics triggered by E. coli exposure. The host response was found to be sensitive to the bacterial exposure rate given the same total bacterial burden.