Model-Informed Translation of In Vitro Effects of Short-, Prolonged- and Continuous-Infusion Meropenem against Pseudomonas aeruginosa to Clinical Settings

Pharmacokinetic-pharmacodynamic (PKPD) models have met increasing interest as tools to identify potential efficacious antibiotic dosing regimens in vitro and in vivo. We sought to investigate the impact of diversely shaped clinical pharmacokinetic profiles of meropenem on the growth/killing patterns of Pseudomonas aeruginosa (ARU552, MIC = 16 mg/L) over time using a semi-mechanistic PKPD model and a PK/PD index-based approach. Bacterial growth/killing were driven by the PK profiles of six patient populations (infected adults, burns, critically ill, neurosurgery, obese patients) given varied pathogen features (e.g., EC50, growth rate, inoculum), patient characteristics (e.g., creatinine clearance), and ten dosing regimens (including two dose levels and 0.5-h, 3-h and continuous-infusion regimens). Conclusions regarding the most favourable dosing regimen depended on the assessment of (i) the total bacterial load or fT>MIC (time that unbound concentrations exceed the minimum inhibitory concentration); (ii) the median or P0.95 profile of the population; and (iii) 8 h or 24 h time points. Continuous infusion plus loading dose as well as 3-h infusions (3-h infusions: e.g., for scenarios associated with low meropenem concentrations, P0.95 profiles, and MIC ≥ 16 mg/L) appeared superior to standard 0.5-h infusions at 24 h. The developed platform can serve to identify promising strategies of efficacious dosing for clinical trials.

Pharmacokinetic-pharmacodynamic models for time courses of antibiotic effects: VSI: Antimicrobial Pharmacometrics

Pharmacokinetic-pharmacodynamic (PKPD) models have emerged as valuable tools for the characterisation and translation of antibiotic effects, and consequently for drug development and therapy. In contrast to traditional PKPD concepts for antibiotics like MIC and PKPD indices, PKPD models enable to describe the continuous, often species- or population-dependent time course of antimicrobial effects, commonly considering mechanistic pathogen- and drug-related knowledge. This review presents a comprehensive overview of previously published PKPD models describing repeated measurements of antibiotic effects. We conducted a literature review to identify PKPD models based on (i) antibiotic compounds, (ii) Gram-positive or Gram-negative pathogens, and (iii) in vitro or in vivo longitudinal colony forming unit data. We identified 132 publications released between 1963 and 2021, including models based on exposure with single antibiotics (n=92) and drug combinations (n=40), as well as different experimental settings (e.g., static/traditional dynamic/hollow-fibre/animal time-kill models, n=90/27/32/11). An interactive, fully searchable table summarises the details of each model, i.e. variants and mechanistic elements of PKPD submodels capturing observed bacterial growth, regrowth, drug effects, and interactions. Furthermore, the review highlights main purposes of PKPD model development, including the translation of preclinical PKPD to clinical settings and the assessment of varied dosing regimens and patient characteristics for their impact on clinical antibiotic effects. In summary, this comprehensive overview of PKPD models shall assist in identifying PKPD modelling strategies to describe growth, killing, regrowth and interaction patterns for pathogen-antibiotic combinations over time and ultimately facilitate model-informed antibiotic translation, dosing and drug development.

A continuous-time multistate Markov model to describe the occurrence and severity of diarrhea events in metastatic breast cancer patients treated with lumretuzumab in combination with pertuzumab and paclitaxel

PURPOSE

To inform lumretuzumab and pertuzumab dose modifications in order to decrease the incidence, severity, and duration of the diarrhea events in metastatic breast cancer patients treated with a combination therapy of lumretuzumab (anti-HER3) in combination with pertuzumab (anti-HER2) and paclitaxel using quantitative clinical pharmacology modeling approaches.

METHODS

The safety and pharmacokinetic (PK) data from three clinical trials (lumretuzumab monotherapy n = 47, pertuzumab monotherapy n = 78, and the combination therapy of lumretuzumab, pertuzumab and paclitaxel n = 35) were pooled together to develop a continuous-time discrete states Markov model describing the dynamics of the diarrhea events.

RESULTS

The model was able to capture the time course of different severities of diarrhea reasonably well. The effect of lumretuzumab and pertuzumab was well described by an E_max function indicating an increased rate of transition from moderate to mild or more severe diarrhea with higher doses. The concentration needed to trigger or worsen diarrhea episodes was estimated to be 120-fold lower in combination therapy compared to monotherapy, suggesting strong synergy between the two monoclonal antibodies. The prophylactic effect of loperamide in a subset of patients was also well captured by the model with a clear tendency to reduce the occurrence of diarrhea events.

CONCLUSIONS

This work shows that PK-toxicity modeling provides insight into how the severity of key adverse events evolves over time and highlights the potential use to support decision making in drug development.

PKPD modeling of acquired resistance to anti-cancer drug treatment

Non-small cell lung cancer (NSCLC) patients greatly benefit from the treatment with tyrosine kinase inhibitors (TKIs) targeting the epidermal growth factor receptor (EGFR). However, emergence of acquired resistance inevitable occurs after long-term treatment in most patients and limits clinical improvement. In the present study, resistance to drug treatment in patient-derived NSCLC xenograft mice was assessed and modeling and simulation was applied to understand the dynamics of drug resistance as a basis to explore more beneficial drug regimen. Two semi-mechanistic models were fitted to tumor growth inhibition profiles during and after treatment with erlotinib or gefitinib. The base model proposes that as a result of drug treatment, tumor cells stop proliferating and undergo several stages of damage before they eventually die. The acquired resistance model adds a resistance term to the base model which assumes that resistant cells are emerging from the pool of damaged tumor cells. As a result, tumor cells sensitive to drug treatment will either die or be converted to a drug resistant cell population which is proliferating at a slower growth rate as compared to the sensitive cells. The observed tumor growth profiles were better described by the resistance model and emergence of resistance was concluded. In simulation studies, the selection of resistant cells was explored as well as the time-variant fraction of resistant over sensitive cells. The proposed model provides insight into the dynamic processes of emerging resistance. It predicts tumor regrowth during treatment driven by the selection of resistant cells and explains why faster tumor regrowth may occur after discontinuation of TKI treatment. Finally, it is shown how the semi-mechanistic model can be used to explore different scenarios and to identify optimal treatment schedules in clinical trials.

A mechanistic PK/PD model for two anti-IL13 antibodies explains the difference in total IL-13 accumulation observed in clinical studies

IMA-638 and IMA-026 are humanized IgG1 monoclonal antibodies (mAbs) that target non-overlapping epitopes of IL-13. Separate first-in-human single ascending dose studies were conducted for each mAb. These studies had similar study designs, but mild to moderate asthmatics were recruited for the IMA-638 study and healthy subjects were recruited for the IMA-026 study. IMA-638 and IMA-026 showed similar pharmacokinetic (PK) profiles, but very different total IL-13 (free and drug bound IL-13) profiles; free IL13 was not measured. IMA-026 treatment induced a dose-dependent accumulation of total IL-13, while IMA-638 treatment led to a much smaller accumulation without any clear dose-response. To understand the differences between the two total IL-13 profiles and to predict the free IL-13 profiles for each mAb treatment, a mechanistic PK/pharmacodynamic model was developed. PK-related parameters were first fit to the mean PK profiles of each mAb separately; thereafter, the target-related parameters were fit to both total IL-13 profiles simultaneously. The IL-13 degradation rate was assumed to be the same for asthma patients and healthy subjects. The model suggests that an approximately 100× faster elimination of IL-13-IMA-638 complex than IL-13-IMA-026 complex could be responsible for the differences observed in total IL-13 profiles for the two mAbs. Furthermore, the model predicts that IMA-638 administration results in greater and more prolonged free IL-13 inhibition than equivalent dosing of IMA-026 despite similar binding KD and PK profile. In conclusion, joint modeling of two similar molecules provided mechanistic insight that the elimination rate of mAb-target complex can regulate the degree of free target inhibition.

PKPD modelling of the interrelationship between mean arterial BP, cardiac output and total peripheral resistance in conscious rats

BACKGROUND AND PURPOSE

The homeostatic control of arterial BP is well understood with changes in BP resulting from changes in cardiac output (CO) and/or total peripheral resistance (TPR). A mechanism-based and quantitative analysis of drug effects on this interrelationship could provide a basis for the prediction of drug effects on BP. Hence, we aimed to develop a mechanism-based pharmacokinetic-pharmacodynamic (PKPD) model in rats that could be used to characterize the effects of cardiovascular drugs with different mechanisms of action (MoA) on the interrelationship between BP, CO and TPR.

EXPERIMENTAL APPROACH

The cardiovascular effects of six drugs with diverse MoA, (amlodipine, fasudil, enalapril, propranolol, hydrochlorothiazide and prazosin) were characterized in spontaneously hypertensive rats. The rats were chronically instrumented with ascending aortic flow probes and/or aortic catheters/radiotransmitters for continuous recording of CO and/or BP. Data were analysed in conjunction with independent information on the time course of drug concentration using a mechanism-based PKPD modelling approach.

KEY RESULTS

By simultaneous analysis of the effects of six different compounds, the dynamics of the interrelationship between BP, CO and TPR were quantified. System-specific parameters could be distinguished from drug-specific parameters indicating that the model developed is drug-independent.

CONCLUSIONS AND IMPLICATIONS

A system-specific model characterizing the interrelationship between BP, CO and TPR was obtained, which can be used to quantify and predict the cardiovascular effects of a drug and to elucidate the MoA for novel compounds. Ultimately, the proposed PKPD model could be used to predict the effects of a particular drug on BP in humans based on preclinical data.