Two-Drug Antimicrobial Chemotherapy: A Mathematical Model and Experiments with Mycobacterium marinum

Multi-drug therapy is the standard-of-care treatment for tuberculosis. Despite this, virtually all studies of the pharmacodynamics (PD) of mycobacterial drugs employed for the design of treatment protocols are restricted to single agents. In this report, mathematical models and in vitro experiments with Mycobacterium marinum and five antimycobacterial drugs are used to quantitatively evaluate the pharmaco-, population and evolutionary dynamics of two-drug antimicrobial chemotherapy regimes. Time kill experiments with single and pairs of antibiotics are used to estimate the parameters and evaluate the fit of Hill-function-based PD models. While Hill functions provide excellent fits for the PD of each single antibiotic studied, rifampin, amikacin, clarithromycin, streptomycin and moxifloxacin, two-drug Hill functions with a unique interaction parameter cannot account for the PD of any of the 10 pairs of these drugs. If we assume two antibiotic-concentration dependent functions for the interaction parameter, one for sub-MIC and one for supra-MIC drug concentrations, the modified biphasic Hill function provides a reasonably good fit for the PD of all 10 pairs of antibiotics studied. Monte Carlo simulations of antibiotic treatment based on the experimentally-determined PD functions are used to evaluate the potential microbiological efficacy (rate of clearance) and evolutionary consequences (likelihood of generating multi-drug resistance) of these different drug combinations as well as their sensitivity to different forms of non-adherence to therapy. These two-drug treatment simulations predict varying outcomes for the different pairs of antibiotics with respect to the aforementioned measures of efficacy. In summary, Hill functions with biphasic drug-drug interaction terms provide accurate analogs for the PD of pairs of antibiotics and M. marinum. The models, experimental protocols and computer simulations used in this study can be applied to evaluate the potential microbiological and evolutionary efficacy of two-drug therapy for any bactericidal antibiotics and bacteria that can be cultured in vitro

The Effects of Cash Flow Management on the Financial Performance of Listed Manufacturing Firms in Ghana

The study investigated the effect of cash flow management on financial performance of listed manufacturing firms in Ghana. Specifically, the study examined the effect of operating cash flows, investing cash flows, financing cash flows and free cash flows on financial performance. The study used a panel data of 10 conveniently selected firms over a 7-year period from 2012 to 2018. Pooled Ordinary Least Squares, Fixed Effect and Random Effect Models to analyze the data. The results indicates that operating cash flows has a negative and statistically significant impact on financial performance, investing cash flows does not have any significant effect on financial performance while financing cash flows has a direct and significant effect on financial performance. Lastly, free cash flow was found to have a positive and significant effect on financial performance of listed manufacturing firms in Ghana. The study concludes that policies by investors or equity holders should not focus entirely on investment cash flows but rather, operating cash flows, financing cash flows and free cash flows as they are found to significantly affect financial performance. Keywords:Cash Flow management, financial performance, operating cash flow, investing cash flow, financing cash flow, free cash flow DOI: 10.7176/RJFA/14-16-05 Publication date:August 31st 2023

Dynamics of non-adherence with therapy.

<p>Changes in the absolute concentrations of the antibiotics and densities of bacteria: S0- sensitive to both drugs, S1- resistant to drug A1, S2- resistant to drug A2, and S12- resistant to both A1 and A2. (a) Random non-adherence: Parameters used are those estimated for clarithromycin + streptomycin, assuming a 20% probability of non-adherence at each dosing. (b) Thermostat non-adherence: Parameters used are those estimated for rifampin + amikacin. (c) Drug holiday non-adherence: Parameters used are those estimated for clarithromycin + moxifloxacin. These figures represent runs in which double resistance (S12) emerged. The relative frequencies of this outcome are shown in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002487#ppat-1002487-t003" target="_blank">Table 3</a>. See the text for descriptions of these different modes of non-adherence.</p

Predicted and observed growth/death rates of <i>M. marinum</i> exposed to different combinations of two antibiotics.

<p>Curves without markers represent predicted theoretical rates, and curves with markers represent observed experimental rates. Values of α represent different degrees of interaction between antibiotics. Positive values indicate synergy, negative values antagonism, and values of zero, additivity. (a) amikacin + clarithromycin (b) amikacin + moxifloxacin (c) amikacin + streptomycin (d) clarithromycin + moxifloxacin (e) clarithromycin + streptomycin (f) rifampin + amikacin (g) rifampin + clarithromycin (h) rifampin + moxifloxacin (i) rifampin + streptomycin (j) streptomycin + moxifloxacin.</p