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Dynamic one-compartment in vitro infection model (“chemostat”). Fresh medium is added continuously while culture contents are removed at the same rate to maintain a constant volume. (A) Chemostat model for simulating a monoexponential decline of drug concentrations after intravenous dosing; the antibiotic(s) is/are dosed into the central reservoir as bolus doses or zero-order infusions. (B) Chemostat for oral dosing, which can simulate drug concentration-time profiles with first-order absorption and elimination; typically, the antibiotic(s) is/are dosed into the antibiotic reservoir as bolus doses.
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In June 2017, The National Institute of Allergy and Infectious Diseases, part of the National Institutes of Health, organized a workshop entitled “Pharmacokinetics-Pharmacodynamics (PK/PD) for Development of Therapeutics against Bacterial Pathogens”. The aims were to discuss details of various PK/PD models and identify sound practices for deriving...
Citations
... Hollow fibre infection models are dynamic two-compartment in vitro models that allow the simulation of in vivo drug exposure [91]. PK data can be collected from these models while accounting for PD factors, making them suitable for preclinical studies [91,92]. ...
Due to variability in pharmacokinetics and pharmacodynamics, clinical outcomes of antimicrobial drug therapy vary between patients. As such, personalised medication management, considering both pharmacokinetics and pharmacodynamics, is a growing concept of interest in the field of infectious diseases. Therapeutic drug monitoring is used to adjust and individualise drug regimens until predefined pharmacokinetic exposure targets are achieved. Minimum inhibitory concentration (drug susceptibility) is the best available pharmacodynamic parameter but is associated with many limitations. Identification of other pharmacodynamic parameters is necessary. Repurposing diagnostic biomarkers as pharmacodynamic parameters to evaluate treatment response is attractive. When combined with therapeutic drug monitoring, it could facilitate making more informed dosing decisions. We believe the approach has potential and justifies further research.
... These methods, while foundational in establishing regulatory standards, are not without limitations. In vivo rodent bioassays, once regarded as the gold standard, pose challenges in terms of resource intensiveness, time consuming, and translatability to human outcomes due to species-specific variations [19]. Similarly, in vitro assays, while reducing animal usage, may not fully capture systemic effects contributing to carcinogenicity [20]. ...
Introduction:
This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance.
Areas covered:
The review comprehensively examines the integration of ML, deep learning, and diverse artificial intelligence (AI) approaches in various aspects of drug development safety assessments. It explores applications ranging from early-phase compound screening to clinical trial optimization, highlighting the versatility of ML in enhancing predictive accuracy and efficiency.
Expert opinion:
Through the analysis of traditional approaches such as in vivo rodent bioassays and in vitro assays, the review underscores the limitations and resource intensity associated with these methods. It provides expert insights into how ML offers innovative solutions to address these challenges, revolutionizing safety assessments in drug development.
... § ELF to plasma ratio based upon AUC0-6 ELF/Plasma [42]. experimental in vitro and in vivo infection models have confirmed these conclusions and have demonstrated that the PK/PD index most closely associated with sulbactam activity was the percentage of time unbound concentrations remain above the MIC or % fT >MIC [16,29,[33][34][35][36][37][38][39]. In vitro studies using a hollow-fiber infection model (HFIM) and a one-compartment chemostat model were used to determine the PK/PD index associated with durlobactam activity using an MDR A. baumannii isolate with a sulbactam MIC of 16 μg/ml and a sulbactam-durlobactam MIC of 4 μg/ml. ...
Sulbactam–durlobactam is a pathogen-targeted β-lactam/β-lactamase inhibitor combination that has been approved by the US FDA for the treatment of hospital-acquired and ventilator-associated bacterial pneumonia caused by susceptible isolates of Acinetobacter baumannii–calcoaceticus complex (ABC) in patients 18 years of age and older. Sulbactam is a penicillin derivative with antibacterial activity against Acinetobacter but is prone to hydrolysis by β-lactamases encoded by contemporary isolates. Durlobactam is a diazabicyclooctane β-lactamase inhibitor with activity against Ambler classes A, C and D serine β-lactamases that restores sulbactam activity both in vitro and in vivo against multidrug-resistant ABC. Sulbactam–durlobactam is a promising alternative therapy for the treatment of serious Acinetobacter infections, which can have high rates of mortality.
... Two of the most cited animal models utilized for the investigation of antibiotics are murine neutropenic thigh infection and murine neutropenic lung infection models [6,7]. These models allow for the characterization of in vivo survival and the optimal PKPD index, which may be used to provide reliable projections for human efficacy [7,8]. Once the optimal PKPD index is established, population PK (popPK) modeling in combination with allometric scaling and probability of target attainment analysis (PTA) could be used to define an ideal range of doses to test in first-in-human (FIH) studies [9]. ...
We sought to better understand the utility and role of animal models of infection for Food and Drug Administration (FDA)-approved antibiotics for the indications of community-, hospital-acquired-, and ventilator-associated bacterial pneumonia (CABP, HABP, VABP), complicated urinary tract infection (cUTI), complicated intra-abdominal infection (cIAI), and acute bacterial skin and structural infections (ABSSSIs). We reviewed relevant documents from new drug applications (NDA) of FDA-approved antibiotics from 2014–2019 for the above indications. Murine neutropenic thigh infection models supported the choice of a pharmacokinetic-pharmacodynamic (PKPD) target in 11/12 NDAs reviewed. PKPD targets associated with at least a 1-log bacterial decrease were commonly considered ideal (10/12 NDAs) to support breakpoints. Plasma PK, as opposed to organ specific PK, was generally considered most reliable for PKPD correlation. Breakpoint determination was multi-disciplinary, accounting at minimum for epidemiologic cutoffs, non-clinical PKPD, clinical exposure-response and clinical efficacy. Non-clinical PKPD targets in combination with probability of target attainment (PTA) analyses generated breakpoints that were consistent with epidemiologic cutoffs and clinically derived breakpoints. In 6/12 NDAs, there was limited data to support clinically derived breakpoints, and hence the non-clinical PKPD targets in combination with PTA analyses played a heightened role in the final breakpoint determination. Sponsor and FDA breakpoint decisions were in general agreement. Disagreement may have arisen from differences in the definition of the optimal PKPD index or the ability to extrapolate protein binding from animals to humans. Overall, murine neutropenic thigh infection models supported the reviewed NDAs by providing evidence of pre-clinical efficacy and PKPD target determination, and played, in combination with PTA analysis, a significant role in breakpoint determination for labeling purposes.
... Best practices have been established to standardize the application of typical in vivo PK/PD models [36][37][38]. The neutropenic murine thigh and lung infection models have demonstrated their ability to predict the clinical exposure targets for multiple established antibiotic classes [39,40], and a positive correlation has been described between clinical The DFS and DRS studies are typically conducted in the neutropenic murine thigh and lung infection model. ...
... The advantage of in vitro models lies in the possibility to simulate human PK profiles, thus allowing testing the effect of the clinical concentration-time profile against isolates of interest in a nonclinical setting, and in the possibility to conduct studies over an extended treatment duration to test suppression of emergence of resistance. A detailed description of the tools used in translational PK/PD is beyond the scope of this review and has been provided in an excellent pair of minireviews by Bulitta and Rizk [35,36], which summarized findings from a workshop entitled "Pharmacokinetics-Pharmacodynamics (PK/PD) for Development of Therapeutics against Bacterial Pathogens" that was organized by the US National Institute of Allergy and Infectious Diseases. ...
... Best practices have been established to standardize the application of typical in vivo PK/PD models [36][37][38]. The neutropenic murine thigh and lung infection models have demonstrated their ability to predict the clinical exposure targets for multiple established antibiotic classes [39,40], and a positive correlation has been described between clinical cure, mortality, and achievement of exposure targets [41]. ...
Antibiotic development traditionally involved large Phase 3 programs, preceded by Phase 2 studies. Recognizing the high unmet medical need for new antibiotics and, in some cases, challenges to conducting large clinical trials, regulators created a streamlined clinical development pathway in which a lean clinical efficacy dataset is complemented by nonclinical data as supportive evidence of efficacy. In this context, translational Pharmacokinetic/Pharmacodynamic (PK/PD) plays a key role and is a major contributor to a “robust” nonclinical package. The classical PK/PD index approach, proven successful for established classes of antibiotics, is at the core of recent antibiotic approvals and the current antibacterial PK/PD guidelines by regulators. Nevertheless, in the case of novel antibiotics with a novel Mechanism of Action (MoA), there is no prior experience with the PK/PD index approach as the basis for translating nonclinical efficacy to clinical outcome, and additional nonclinical studies and PK/PD analyses might be considered to increase confidence. In this review, we discuss the value and limitations of the classical PK/PD approach and present potential risk mitigation activities, including the introduction of a semi-mechanism-based PK/PD modeling approach. We propose a general nonclinical PK/PD package from which drug developers might choose the studies most relevant for each individual candidate in order to build up a “robust” nonclinical PK/PD understanding.
... Moreover, future research is warranted to assess the impact of pH and different oxygen tensions on aminoglycoside efficacy for lung infections (74,75). This study supports translational research to simulate the time course of ELF concentrations in in vitro infection models (76) and future clinical ELF penetration studies in animals and humans. (23,24,(26)(27)(28)(29)(30)(31). ...
Aminoglycosides are important treatment options for serious lung infections, but modeling analyses to quantify their human lung epithelial lining fluid (ELF) penetration are lacking. We estimated the extent and rate of penetration for five aminoglycosides via population pharmacokinetics from eight published studies. The area under the curve in ELF vs plasma ranged from 50% to 100% and equilibration half-lives from 0.61 to 5.80 h, indicating extensive system hysteresis. Aminoglycoside ELF peak concentrations were blunted, but overall exposures were moderately high.
... Antimicrobial PKPD is founded on decades of research in in vitro models, animal models, and patients [10,12,43,44]. Unlike medications with a direct therapeutic effect in patients, antibiotics work through their activity against an infecting pathogen. ...
Appropriate surgical antimicrobial prophylaxis (SAP) is an important measure in preventing surgical site infections (SSIs). Although antimicrobial pharmacokinetics–pharmacodynamics (PKPD) is integral to optimizing antibiotic dosing for the treatment of infections, there is less research on preventing infections postsurgery. Whereas clinical studies of SAP dose, preincision timing, and redosing are informative, it is difficult to isolate their effect on SSI outcomes. Antimicrobial PKPD aims to explain the complex relationship between antibiotic exposure during surgery and the subsequent development of SSI. It accounts for the many factors that influence the PKs and antibiotic concentrations in patients and considers the susceptibilities of bacteria most likely to contaminate the surgical site. This narrative review examines the relevance and role of PKPD in providing effective SAP. The dose–response relationship i.e., association between lower dose and SSI in cefazolin prophylaxis is discussed. A comprehensive review of the evidence for an antibiotic concentration–response (SSI) relationship in SAP is also presented. Finally, PKPD considerations for improving SAP are explored with a focus on cefazolin prophylaxis in adults and outstanding questions regarding its dose, preincision timing, and redosing during surgery.
... Mathematical modelling plays an important role in exploring the dynamics of microbial growth, antimicrobial pharmacokinetics (the absorption, distribution and elimination of the drug in the body) and pharmacodynamics (the drug's effect on the microbial population) (Nielsen and Friberg 2013;Vinks et al. 2014). Coupled with experimental laboratory work and clinical studies, mathematical modelling aids in the design and evaluation of treatment protocols and guidelines (Bulitta et al. 2019;Rao and Landersdorfer 2021;Rayner et al. 2021). ...
This work studies fundamental questions regarding the optimal design of antimicrobial treatment protocols, using pharmacodynamic and pharmacokinetic mathematical models. We consider the problem of designing an antimicrobial treatment schedule to achieve eradication of a microbial infection, while minimizing the area under the time-concentration curve (AUC), which is equivalent to minimizing the cumulative dosage. We first solve this problem under the assumption that an arbitrary antimicrobial concentration profile may be chosen, and prove that the ideal concentration profile consists of a constant concentration over a finite time duration, where explicit expressions for the optimal concentration and the time duration are given in terms of the pharmacodynamic parameters. Since antimicrobial concentration profiles are induced by a dosing schedule and the antimicrobial pharmacokinetics, the ‘ideal’ concentration profile is not strictly feasible. We therefore also investigate the possibility of achieving outcomes which are close to those provided by the ‘ideal’ concentration profile, using a bolus+continuous dosing schedule, which consists of a loading dose followed by infusion of the antimicrobial at a constant rate. We explicitly find the optimal bolus+continuous dosing schedule, and show that, for realistic parameter ranges, this schedule achieves results which are nearly as efficient as those attained by the ‘ideal’ concentration profile. The optimality results obtained here provide a baseline and reference point for comparison and evaluation of antimicrobial treatment plans.
... To support modeling of the emergence of bacterial resistance, it is essential to inform such models by data on both the total bacterial population and on less susceptible populations growing on antibiotic-containing agar, 10,29,37,41,42 as done in our prospective validation SCTK studies (Figs. 4 and 5). Of note, the regrowth of the total population was considerably more extensive and faster than the emergence of colonies growing on colistin-containing agar. ...
... Data from animal-based pharmacokinetic, pharmacodynamic, and toxicologic studies, exploratory in vitro and in vivo mechanistic studies, organ-on-chip and multi-organ chip systems, and cell assay platforms may be used to evaluate toxicity, explore mechanical models, and develop in vivo predictive models. [222][223][224][225][226] Pharmacokinetics (PK) studies show drugs are absorbed, distributed, metabolized, and excreted over time. Pharmacodynamics (PD) investigates how drugs affect the body biologically. ...
Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years—from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.
