Applications of pharmacometrics in the clinical development and pharmacotherapy of anti-infectives

The distinctive pharmacokinetic profile of rezafungin, a long-acting echinocandin developed in the era of modern pharmacometrics

Echinocandin drugs are the current first-line therapy for fungal infections caused by Candida spp. Most patients require once-daily intravenous (IV) administration in a hospital or outpatient setting for treatment, which may negatively impact their quality of life and stress healthcare resources. Similar to other echinocandins, the novel FDA-, EMA-, and Medical and Healthcare Products Regulatory Agency-approved echinocandin, rezafungin (CD101), exhibited strong antifungal activity against several fungal pathogens and a low drug-drug interaction liability, which are important for medically complex patients. A pharmacometric-based approach has been adopted throughout the development of rezafungin, which contrasts with older echinocandins where dosing regimens were largely derived empirically, and only recently based on pharmacometric guidance. This state-of-the-art approach used model-based simulations incorporating pre-clinical and clinical data as it became available to optimize the dosing regimen for rezafungin. The enhanced stability of the molecular structure and the safety profile of rezafungin allow for the administration of once-weekly IV doses, compared to the daily dosing requirement for other echinocandin drugs, with this distinctive pharmacokinetic profile of rezafungin resulting in a front-loaded dosing regimen with high exposures early in therapy for enhanced fungal killing. The long shelf-life of rezafungin makes this echinocandin more flexible in terms of storage and manufacturing. Demonstrated across clinical development, rezafungin may provide patients with next-generation first-line antifungal treatment for the treatment of candidaemia and invasive candidiasis.

A randomized trial to assess the acceleration of viral clearance by the combination Favipiravir/Ivermectin/Niclosamide in mild-to-moderate COVID-19 adult patients (FINCOV)

BACKGROUND

The efficacy of the viral clearance and clinical outcomes of favipiravir (FPV) in outpatients being treated for coronavirus disease 2019 (COVID-19) is unclear. Ivermectin (IVM), niclosamide (NCL), and FPV demonstrated synergistic effects in vitro for exceed 78% inhibiting severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) replication.

METHODS

A phase 2, open-label, 1:1, randomized, controlled trial was conducted on Thai patients with mild-to-moderate COVID-19 who received either combination FPV/IVM/NCL therapy or FPV alone to assess the rate of viral clearance among individuals with mild-to-moderate COVID-19.

RESULTS

Sixty non-high-risk comorbid patients with mild-to-moderate COVID-19 were randomized; 30 received FPV/IVM/NCL, and 30 received FPV alone. Mixed-effects multiple linear regression analysis of the cycle threshold value from SARS-CoV-2 PCR demonstrated no statistically significant differences in viral clearance rates between the combined FPV/IVM/NCL therapy group and the FPV-alone group. World Health Organization Clinical Progression scores and symptomatic improvement did not differ between arms on days 3, 6, and 10, and no adverse events were reported. No patients required hospitalization, intensive care unit admission, or supplemental oxygen or died within 28 days. C-reactive protein on day 3 was lower in the FPV/IVM/NCL group.

CONCLUSION

Viral clearance rates did not differ significantly between the FPV/IVM/NCL combination therapy and FPV-alone groups of individuals with mild-to-moderate COVID-19, although the combined regimen demonstrated a synergistic effect in vitro. No discernible clinical benefit was observed. Further research is required to explore the potential benefits of FVP beyond its antiviral effects.

TRIAL REGISTRATION

TCTR20230403007, Registered 3 April 2023 - Retrospectively registered,https://trialsearch.who.int/Trial2.aspx?TrialID=TCTR20230403007.

The importance of pharmacokinetics and pharmacodynamics in antimicrobial drug development and their influence on the success of agents developed to combat resistant gram negative pathogens: A review

A deep understanding of an antimicrobial’s critical pharmacokinetic and pharmacodynamic properties is crucial towards optimizing its use in patients and bolstering the drug development program. With the growing threat of antimicrobial resistance and decline in antimicrobial development, the advancement of complex and rigorous pharmacokinetic and pharmacodynamic studies over a short time span has renewed confidence in the value of pharmacokinetic and pharmacodynamic studies and allowed it to become fundamental component of a robust drug development program with high chances of successful approval. In addition, recent guidance by various regulatory bodies have reinforced that a strong and dedicated focus on pharmacokinetics and pharmacodynamics throughout research and development lead to the use of an optimized dosing regimen in Phase 3 trials, improving the probability of drug approval. The objective of this review is to demonstrate the importance of pharmacokinetic and pharmacodynamic studies in the drug development decision-making process by highlighting the developments in pharmacokinetic and pharmacodynamic methods and discuss the role of pharmacokinetic and pharmacodynamic studies in antimicrobial successes and failures.

Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin

Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but offer less mechanistic insights. The opportunity of using ML predictions of drug PK as input for a PKPD model could strongly accelerate analysis efforts. Here exemplified by rifampicin, a widely used antibiotic, we explore the ability of different ML algorithms to predict drug PK. Based on simulated data, we trained linear regressions (LASSO), Gradient Boosting Machines, XGBoost and Random Forest to predict the plasma concentration-time series and rifampicin area under the concentration-versus-time curve from 0–24 h (AUC_0–24h) after repeated dosing. XGBoost performed best for prediction of the entire PK series (R²: 0.84, root mean square error (RMSE): 6.9 mg/L, mean absolute error (MAE): 4.0 mg/L) for the scenario with the largest data size. For AUC_0–24h prediction, LASSO showed the highest performance (R²: 0.97, RMSE: 29.1 h·mg/L, MAE: 18.8 h·mg/L). Increasing the number of plasma concentrations per patient (0, 2 or 6 concentrations per occasion) improved model performance. For example, for AUC_0–24h prediction using LASSO, the R² was 0.41, 0.69 and 0.97 when using predictors only (no plasma concentrations), 2 or 6 plasma concentrations per occasion as input, respectively. Run times for the ML models ranged from 1.0 s to 8 min, while the run time for the PM model was more than 3 h. Furthermore, building a PM model is more time- and labor-intensive compared with ML. ML predictions of drug PK could thus be used as input into a PKPD model, enabling time-efficient analysis.

Pharmacokinetics, Pharmacodynamics, and Dose Optimization of Cefiderocol during Continuous Renal Replacement Therapy

BACKGROUND

The need for continuous renal replacement therapy (CRRT) in critically ill patients with serious infections is associated with clinical failure, emergence of resistance, and excess mortality. These poor outcomes are attributable in large part to subtherapeutic antimicrobial exposure and failure to achieve target pharmacokinetic/pharmacodynamic (PK/PD) thresholds during CRRT. Cefiderocol is a novel siderophore cephalosporin with broad in vitro activity against resistant pathogens and is often used to treat critically ill patients, including those receiving CRRT, despite the lack of data to guide dosing in this population.

OBJECTIVE

The aim of this study was to evaluate the PK and PD of cefiderocol during in vitro and in vivo CRRT and provide optimal dosing recommendations.

METHODS

The PK and dialytic clearance of cefiderocol was evaluated via an established in vitro CRRT model across various modes, filter types, and effluent flow rates. These data were combined with in vivo PK data from nine patients receiving cefiderocol while receiving CRRT from phase III clinical trials. Optimal dosing regimens and their respective probability of target attainment (PTA) were assessed via an established population PK model with Bayesian estimation and 1000-subject Monte Carlo simulations at each effluent flow rate.

RESULTS

The overall mean sieving/saturation coefficient during in vitro CRRT was 0.90 across all modes, filter types, effluent flow rates, and points of replacement fluid dilution tested. Adsorption was negligible at 10.9%. Three-way analysis of variance (ANOVA) and multiple linear regression analyses demonstrated that effluent flow rate is the primary driver of clearance during CRRT and can be used to calculate optimal cefiderocol doses required to match the systemic exposure observed in patients with normal renal function. Bayesian estimation of these effluent flow rate-based optimal doses in nine patients receiving CRRT from the phase III clinical trials of cefiderocol revealed comparable mean (± standard deviation) area under the concentration-time curve values as patients with normal renal function (1709 ± 539 mg·h/L vs. 1494 ± 58.4 mg·h/L; p = 0.26). Monte Carlo simulations confirmed these doses achieved >90% PTA against minimum inhibitory concentrations ≤4 mg/L at effluent flow rates from 0.5 to 5 L/h.

CONCLUSION

The optimal dosing regimens developed from this work have been incorporated into the prescribing information for cefiderocol, making it the first and only antimicrobial with labeled dosing for CRRT. Future clinical studies are warranted to confirm the efficacy and safety of these regimens.

Decoding (patho-)physiology of the lung by advanced in vitro models for developing novel anti-infectives therapies

Advanced lung cell culture models provide physiologically-relevant and complex data for mathematical models to exploit host-pathogen responses during anti-infective drug testing.

Therapeutic drug monitoring of commonly used anti-infective agents: A nationwide cross-sectional survey of Australian hospital practices

When performed according to best-practice principles, therapeutic drug monitoring (TDM) can optimise anti-infective treatment and directly benefit clinical outcomes. We evaluated TDM performance and clinical decision-making for established anti-infective agents amongst Australian hospitals. A nationwide cross-sectional survey was conducted between August and September 2019. The survey consisted of multiple-choice questions regarding TDM of anti-infective agents in general as well as clinical vignettes specific to vancomycin, gentamicin and voriconazole. We sought to survey all Australian hospitals operating both in the public and private health sectors. Responses were captured from 85 unique institutions, from all Australian states and territories. Regarding guidelines, 26% of hospitals did not have endorsed guidelines to advise on the ordering, sampling and interpretation of TDM for any anti-infective agent. Admitting teams were predominantly responsible for ordering TDM (85%) and interpreting results (76%). Only 51% of hospitals had access to dose prediction software, with access generally better amongst principal referral (69%) (P = 0.01) and children's hospitals (100%) (P = 0.04). Whenever a laboratory-derived minimum inhibitory concentration (MIC) was not available to guide dosing decisions, a surrogate target MIC was assumed in 77% of hospitals. This was based on a 'worst-case' scenario infection in 11% of hospitals. The rates of clinical practice consistent with current guideline recommendations across all aspects of TDM were demonstrated to be 0% for vancomycin, 4% for gentamicin and 35% for voriconazole. At present, there is significant institutional variability in the clinical practice of TDM for anti-infective agents in Australia for established TDM drugs.

Dynamic time-kill curve characterization of spectinamide antibiotics 1445 and 1599 for the treatment of tuberculosis

Spectinamides are a novel class of antibiotics under development for the treatment of MDR- and XDR-tuberculosis, with 1599 and 1445 as early lead candidates within this group. In order to evaluate and differentiate the pharmacological properties of these compounds and assist in candidate selection and design of optimal dosing regimens in animal models of Mtb infection, time kill curve assessments were performed in a previously established in vitro PK/PD model system. The performed studies and subsequent pharmacometric analysis indicate that the anti-mycobacterial activity of 1599 exhibits concentration-dependent killing whereas 1445 shows time-dependent killing. These findings are supported by the fact that the PKPD index that best describes bacterial killing is T > MIC for 1445, but fC_max/AUC for 1599. The differential killing behavior among the lead candidates can be rationalized by the differences in post-antibiotic effect: 15.7 h for 1445 compared the 133 h for 1599. Overall, the PK/PD based analysis of the in vitro pharmacologic killing profile of spectinamides 1599 and 1445 on mycobacteria provided valuable insights that contributed to lead candidate selection and preclinical development of these compounds.

Is It Time for Going Beyond the P-Value Paradigm With the Estimation of the Probability of Clinical Benefit as a Criterion for Assessing the Outcomes of a Clinical Trial? A Case Study in Patients With Major Depressive Disorder

The conventional statistical methodologies for evaluating treatment effect are based on hypothesis testing (P-value). Alternative measurements of treatment effect have been proposed for anti-infective treatments using the probability of target attainment. A general framework is proposed to extend this methodology to other therapeutic areas. A disease trial model is used for estimating the probability of reaching a treatment effect associated with relevant clinical benefits, in complement to the evaluation of the probability of rejecting the null hypothesis. A case study is presented in depression, where disease status is evaluated using bounded clinical scores (Hamilton Depression Rating Scale), and detectable treatment effect is inversely proportional to placebo response. The β-regression approach is used to model Hamilton scale scores, and a placebo-related criterion is proposed for determining the clinical benefit. The probability of reaching a clinical benefit represents a reliable criterion for replacing the P-value paradigm in the assessment of the outcomes of clinical trials.

Key Components for Antibiotic Dose Optimization of Sepsis in Neonates and Infants

Sepsis in neonates and infants remains a major cause of death despite a decline in child mortality and morbidity over the last decades. A key factor in further reducing poor clinical outcomes is the optimal use of antibiotics in sepsis management. Developmental changes such as maturation of organ function and capacity of drug metabolizing enzymes can affect the pharmacokinetic profile and therefore the antibiotic exposure and response in neonates and infants. Optimal antibiotic treatment of sepsis in neonates and young infants is dependent on several key components such as the determination of treatment phase, the administered dose and the resulted drug exposure and microbiological response. During the initial phase of suspected sepsis, the primary focus of empirical treatment is to assure efficacy. Once bacterial infection as the cause of sepsis is confirmed the focus shifts toward a targeted treatment, ensuring an optimal balance between efficacy and safety. Interpretation of antibiotic exposure and microbiological response in neonates and infants is multifaceted. The response or treatment effect can be determined by the microbiological parameters (MIC) together with the characteristics of the pathogen (time- or concentration dependent). The antibiotic response is influenced by the properties of the causative pathogen and the unique characteristics of the vulnerable patient population such as reduced humoral response or reduced skin barrier function. Therapeutic drug monitoring (TDM) of antibiotics may be used to increase effectiveness while maximizing safety and minimizing the toxicity, but requires expertise in different fields and requires collaborations between physicians, lab technicians, and quantitative clinical pharmacologists. Understanding these clinical, pharmacological, and microbiological components and their underlying relationship can provide a scientific basic for proper antibiotic use and reduction of antibiotic resistance in neonates and infants. This highlights the necessity of a close multidisciplinary collaboration between physicians, pharmacists, clinical pharmacologists and microbiologist to assure the optimal utilization of antibiotics in neonates and young infants.

Drug-drug interaction discovery and demystification using Semantic Web technologies

Objective

To develop a novel pharmacovigilance inferential framework to infer mechanistic explanations for asserted drug-drug interactions (DDIs) and deduce potential DDIs.

Materials and Methods

A mechanism-based DDI knowledge base was constructed by integrating knowledge from several existing sources at the pharmacokinetic, pharmacodynamic, pharmacogenetic, and multipathway interaction levels. A query-based framework was then created to utilize this integrated knowledge base in conjunction with 9 inference rules to infer mechanistic explanations for asserted DDIs and deduce potential DDIs.

Results

The drug-drug interactions discovery and demystification (D3) system achieved an overall 85% recall rate in terms of inferring mechanistic explanations for the DDIs integrated into its knowledge base, while demonstrating a 61% precision rate in terms of the inference or lack of inference of mechanistic explanations for a balanced, randomly selected collection of interacting and noninteracting drug pairs.

Discussion

The successful demonstration of the D3 system's ability to confirm interactions involving well-studied drugs enhances confidence in its ability to deduce interactions involving less-studied drugs. In its demonstration, the D3 system infers putative explanations for most of its integrated DDIs. Further enhancements to this work in the future might include ranking interaction mechanisms based on likelihood of applicability, determining the likelihood of deduced DDIs, and making the framework publicly available.

Conclusion

The D3 system provides an early-warning framework for augmenting knowledge of known DDIs and deducing unknown DDIs. It shows promise in suggesting interaction pathways of research and evaluation interest and aiding clinicians in evaluating and adjusting courses of drug therapy.

Parameter estimation for sigmoid Emax models in exposure-response relationship

The purpose of this simulation study is to explore the limitation of the population PK/PD analysis using data from a clinical study and to help to construct an appropriate PK/PD design that enable precise and unbiased estimation of both fixed and random PD parameters in PK/PD analysis under different doses and Hill coefficients. Seven escalating doses of virtual drugs with equal potency and efficacy but with five different Hill coefficients were used in simulations of single and multiple dose scenarios with dense sampling design. A total of 70 scenarios with 100 subjects were simulated and estimated 100 times applying 1-compartment PK model and sigmoid E_max model. The bias and precision of the parameter estimates in each scenario were assessed using relative bias and relative root mean square error. For the single dose scenarios, most PD parameters of sigmoid E_max model were accurately and precisely estimated when the C_max was more than 85% of EC₅₀, except for typical value and inter-individual variability of EC₅₀ which were poorly estimated at low Hill coefficients. For the multiple dose studies, the parameter estimation performance was not good. This simulation study demonstrated the effect of the relative range of sampled concentrations to EC₅₀ and sigmoidicity on the parameter estimation performance using dense sampling design.

Translational PK/PD of anti-infective therapeutics

Translational PK/PD modeling has emerged as a critical technique for quantitative analysis of the relationship between dose, exposure and response of antibiotics. By combining model components for pharmacokinetics, bacterial growth kinetics and concentration-dependent drug effects, these models are able to quantitatively capture and simulate the complex interplay between antibiotic, bacterium and host organism. Fine-tuning of these basic model structures allows to further account for complicating factors such as resistance development, combination therapy, or host responses. With this tool set at hand, mechanism-based PK/PD modeling and simulation allows to develop optimal dosing regimens for novel and established antibiotics for maximum efficacy and minimal resistance development.

Multiscale Modeling in the Clinic: Drug Design and Development

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.

Cryptosporidiosis Drug Discovery: Opportunities and Challenges

The apicomplexan parasite Cryptosporidium is the second most important diarrheal pathogen causing life-threatening diarrhea in children, which is also associated with long-term growth faltering and cognitive deficiency. Cryptosporidiosis is a parasitic disease of public health concern caused by Cryptosporidium parvum and Cryptosporidium hominis. Currently, nitazoxanide is the only approved treatment for cryptosporidium infections. Unfortunately, it has limited efficacy in the most vulnerable patients, thus there is an urgent need for a safe and efficacious cryptosporidiosis drug. In this work, we present our current perspectives on the target product profile for novel cryptosporidiosis therapies and the perceived challenges and possible mitigation plans at different stages in the cryptosporidiosis drug discovery process.

Applications of physiologically based pharmacokinetic modeling for the optimization of anti-infective therapies

INTRODUCTION

The pharmacokinetic properties of anti-infective drugs are a determinant part of treatment success. Pathogen replication is inhibited if adequate drug levels are achieved in target sites, whereas excessive drug concentrations linked to toxicity are to be avoided. Anti-infective distribution can be predicted by integrating in vitro drug properties and mathematical descriptions of human anatomy in physiologically based pharmacokinetic models. This method reduces the need for animal and human studies and is used increasingly in drug development and simulation of clinical scenario such as, for instance, drug-drug interactions, dose optimization, novel formulations and pharmacokinetics in special populations.

AREAS COVERED

We have assessed the relevance of physiologically based pharmacokinetic modeling in the anti-infective research field, giving an overview of mechanisms involved in model design and have suggested strategies for future applications of physiologically based pharmacokinetic models.

EXPERT OPINION

Physiologically based pharmacokinetic modeling provides a powerful tool in anti-infective optimization, and there is now no doubt that both industry and regulatory bodies have recognized the importance of this technology. It should be acknowledged, however, that major challenges remain to be addressed and that information detailing disease group physiology and anti-infective pharmacodynamics is required if a personalized medicine approach is to be achieved.