In vitro model to simulate multiple drugs with distinct elimination half-lives.

Objective: The prevalence of drug resistance in pathogens such as HIV and selected bacteria has been steadily rising, resulting in an increased need for using multiple agents concurrently. Agents used in these combination therapies may have different elimination half-lives in humans. There is an unmet need for in vitro models to evaluate the efficacy of these combinations to guide early drug development.

Rapid In Vitro Assessment of Antimicrobial Drug Effect Bridging Clinically Relevant Pharmacokinetics: A Comprehensive Methodology.

Rapid in vitro assessment of antimicrobial drug efficacy under clinically relevant pharmacokinetic conditions is an essential element of both drug development and clinical use. Here, we present a comprehensive overview of a recently developed novel integrated methodology for rapid assessment of such efficacy, particularly against the emergence of resistant bacterial strains, as jointly researched by the authors in recent years. This methodology enables rapid in vitro assessment of the antimicrobial efficacy of single or multiple drugs in combination, following clinically relevant pharmacokinetics.

Systematic design of pulse dosing to eradicate persister bacteria.

A small fraction of infectious bacteria use persistence as a strategy to survive exposure to antibiotics. Periodic pulse dosing of antibiotics has long been considered a potentially effective strategy towards eradication of persisters. Recent studies have demonstrated through in vitro experiments that it is indeed feasible to achieve such effectiveness.

Deciphering longitudinal optical-density measurements to guide clinical dosing regimen design: A model-based approach.

Background: Model-based analysis of longitudinal optical density measurements from a bacterial suspension exposed to antibiotics has been proposed as a potentially efficient and effective method for extracting useful information to improve the individualized design of treatments for bacterial infections. To that end, the authors developed in previous work a mathematical modeling framework that can use such measurements for design of effective dosing regimens.

Objectives: Here we further explore ways to extract information from longitudinal optical density measurements to predict bactericidal efficacy of clinically relevant antibiotic exposures.

Revisiting the standard for modeling the spread of infectious diseases.

The COVID-19 epidemic brought to the forefront the value of mathematical modelling for infectious diseases as a guide to help manage a formidable challenge for human health. A standard dynamic model widely used for a spreading epidemic separates a population into compartments-each comprising individuals at a similar stage before, during, or after infection-and keeps track of the population fraction in each compartment over time, by balancing compartment loading, discharge, and accumulation rates. The standard model provides valuable insight into when an epidemic spreads or what fraction of a population will have been infected by the epidemic's end.

Discerning in vitro pharmacodynamics from OD measurements: A model-based approach.

Time-kill experiments can discern the pharmacodynamics of infectious bacteria exposed to antibiotics in vitro, and thus help guide the design of effective therapies for challenging clinical infections. This task is resource-limited, therefore typically bypassed in favor of empirical shortcuts. The resource limitation could be addressed by continuously assessing the size of a bacterial population under antibiotic exposure using optical density measurements.

Ziegler and Nichols meet Kermack and McKendrick: Parsimony in dynamic models for epidemiology.

The COVID-19 crisis popularized the importance of mathematical modeling for managing epidemics. A celebrated pertinent model was developed by Kermack and McKendrick about a century ago. A simplified version of that model has long been used and became widely popular recently, even though it has limitations that its originators had clearly articulated and warned against.

SIMULTANEOUS IN VITRO SIMULATION OF MULTIPLE ANTIMICROBIAL AGENTS WITH DIFFERENT ELIMINATION HALF-LIVES IN A PRE-CLINICAL INFECTION MODEL.

Combination therapy for treatment of multi-drug resistant bacterial infections is becoming common. In vitro testing of drug combinations under realistic pharmacokinetic conditions is needed before a corresponding combination is eventually put into clinical use. The current standard for design of such in vitro simulations for drugs with different half-lives is heuristic and limited to two drugs.

Experimental Validation of a Mathematical Framework to Simulate Antibiotics with Distinct Half-Lives Concurrently in an In Vitro Model.

Antimicrobial resistance has been steadily increasing in prevalence, and combination therapy is commonly used to treat infections due to multidrug resistant bacteria. Under certain circumstances, combination therapy of three or more drugs may be necessary, which makes it necessary to simulate the pharmacokinetic profiles of more than two drugs concurrently in vitro. Recently, a general theoretical framework was developed to simulate three drugs with distinctly different half-lives.

The Lambert Function Should Be in the Engineering Mathematical Toolbox.

Discovered well over two centuries ago and little used for long, the Lambert function has emerged in an increasing number of science and engineering applications in the last couple of decades. Here we present case studies relevant to the diverse interests of chemical engineers. We show how the Lambert function can be used for both analysis and computation.

Optimizing pharmacokinetics/pharmacodynamics of β-lactam/β-lactamase inhibitor combinations against high inocula of ESBL-producing bacteria.

Objectives: Reduced in vitro β-lactam activity against a dense bacterial population is well recognized. It is commonly attributed to the presence of β-lactamase(s) and it is unknown whether the inoculum effect could be diminished by a β-lactamase inhibitor. We evaluated different β-lactam/β-lactamase inhibitor combinations in suppressing a high inoculum of ESBL-producing bacteria.

Azasteroid Alkylators as Dual Inhibitors of AKT and ERK Signaling for the Treatment of Ovarian Carcinoma.

(1) Background: Previous findings show that lactam steroidal alkylating esters display improved therapeutic efficacy with reduced toxicity. The aim of this study was to evaluate the anticancer activity of two newly synthesized aza-steroid alkylators (ENGA-L06E and ENGA-L08E) against human ovarian carcinoma cells, and consequently, the dual inhibition of RAS/PI3K/AKT and RAS/RAF/MEK/ERK signaling pathways, both of which are closely associated with ovarian cancer; (2) Methods: The in vitro cytostatic and cytotoxic effects of ENGA-L06E and ENGA-L08E were evaluated in a panel of five human ovarian cancer cell lines, as well as in in vivo studies. ENGA-L06E and ENGA-L08E, in addition to another two aniline-mustard alkylators, POPAM and melphalan (L-PAM), were utilized in order to determine the acute toxicity and antitumor efficacy on two human ovarian xenograft models.

Modeling Heterogeneous Bacterial Populations Exposed to Antibiotics: The Logistic-Dynamics Case.

In typical in vitro tests for clinical use or development of antibiotics, samples from a bacterial population are exposed to an antibiotic at various concentrations. The resulting data can then be used to build a mathematical model suitable for dosing regimen design or for further development. For bacterial populations that include resistant subpopulations-an issue that has reached alarming proportions-building such a model is challenging.

Mathematical model to quantify the effects of risk factors on carbapenem-resistant Acinetobacter baumannii.

Carbapenem-resistant Acinetobacter baumannii (CRAB) infections are increasing, and they are associated with an increased risk of mortality in hospitalized patients. Linear regression is commonly used to identify concurrent trends, but it cannot quantify the relationship between risk factors and resistance. We developed a model to quantify the impact of antibiotic consumption on the prevalence of CRAB over time.

Novel modeling framework to guide design of optimal dosing strategies for β-lactamase inhibitors.

The scarcity of new antibiotics against drug-resistant bacteria has led to the development of inhibitors targeting specific resistance mechanisms, which aim to restore the effectiveness of existing agents. However, there are few guidelines for the optimal dosing of inhibitors. Extending the utility of mathematical modeling, which has been used as a decision support tool for antibiotic dosing regimen design, we developed a novel mathematical modeling framework to guide optimal dosing strategies for a beta-lactamase inhibitor.

Predicting bacterial fitness cost associated with drug resistance.

Objectives: It has been proposed that antimicrobial resistance could be associated with a fitness cost in bacteria, which is often determined by competition experiments between isogenic strains (wild-type and mutant). However, this conventional approach is time consuming and labour intensive. An alternative method was developed to assess the fitness cost in drug-resistant bacteria.

Quantitative impact of neutrophils on bacterial clearance in a murine pneumonia model.

The rapid increase in the prevalence of antibiotic-resistant pathogens is a global problem that has challenged our ability to treat serious infections. Currently, clinical decisions on treatment are often based on in vitro susceptibility data. The role of the immune system in combating bacterial infections is unequivocal, but it is not well captured quantitatively.

Modelling biphasic killing of fluoroquinolones: guiding optimal dosing regimen design.

Objectives: Fluoroquinolones are commonly believed to exhibit concentration-dependent killing, but time-kill studies have revealed that fluoroquinolone activity could be a complex combination of concentration-dependent and -independent killing. We had previously developed a mathematical modelling framework to describe the dynamics of bacterial populations under the effect of antimicrobials, which could facilitate the design of optimal dosing regimens. Our objective was to extend the framework to describe the effect of fluoroquinolones on heterogeneous populations of Escherichia coli and Staphylococcus aureus.

A novel approach to pharmacodynamic assessment of antimicrobial agents: new insights to dosing regimen design.

Pharmacodynamic modeling has been increasingly used as a decision support tool to guide dosing regimen selection, both in the drug development and clinical settings. Killing by antimicrobial agents has been traditionally classified categorically as concentration-dependent (which would favor less fractionating regimens) or time-dependent (for which more frequent dosing is preferred). While intuitive and useful to explain empiric data, a more informative approach is necessary to provide a robust assessment of pharmacodynamic profiles in situations other than the extremes of the spectrum (e.

Mathematical modeling to characterize the inoculum effect.

Killing by beta-lactams is well known to be reduced against a dense bacterial population, commonly known as the inoculum effect. However, the underlying mechanism of this phenomenon is not well understood. We proposed a semi-mechanistic mathematical model to account for the reduced in vitro killing observed.

The fluid mechanics of scleral buckling surgery for the repair of retinal detachment.

Background: Scleral buckling is a common surgical technique used to treat retinal detachments that involves suturing a radial or circumferential silicone element on the sclera. Although this procedure has been performed since the 1960s, and there is a reasonable experimental model of retinal detachment, there is still debate as to how this surgery facilitates the re-attachment of the retina.

Methods: Finite element calculations using the COMSOL Multiphysics system are utilized to explain the influence of the scleral buckle on the flow of sub-retinal fluid in a physical model of retinal detachment.

Pharmacodynamic modeling of aminoglycosides against Pseudomonas aeruginosa and Acinetobacter baumannii: identifying dosing regimens to suppress resistance development.

To facilitate optimal dosing regimen design, we previously developed a mathematical model using time-kill study data to predict the responses of Pseudomonas aeruginosa to various pharmacokinetic profiles of meropenem and levofloxacin. In this study, we extended the model to predict the activities of gentamicin and amikacin exposures against P. aeruginosa and Acinetobacter baumannii, respectively.

Quantitative assessment of combination antimicrobial therapy against multidrug-resistant Acinetobacter baumannii.

Treatment of multidrug-resistant bacterial infections poses a therapeutic challenge to clinicians; combination therapy is often the only viable option for multidrug-resistant infections. A quantitative method was developed to assess the combined killing abilities of antimicrobial agents. Time-kill studies (TKS) were performed using a multidrug-resistant clinical isolate of Acinetobacter baumannii with escalating concentrations of cefepime (0 to 512 mg/liter), amikacin (0 to 256 mg/liter), and levofloxacin (0 to 64 mg/liter).

Mathematical modelling response of Pseudomonas aeruginosa to meropenem.

Objectives: Widespread emergence of resistance to antimicrobial agents is a serious problem. The rate at which new agents are made available clinically is unlikely to keep up with these resistant pathogens, and there is an urgent need to accelerate antimicrobial agent development. We explored the use of mathematical modelling to guide selection of dosing regimens.