A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin

Tumor heterogeneity contributes significantly to chemoresistance, a leading cause of treatment failure. To better personalize therapies, it is essential to develop tools capable of identifying and predicting intra- and inter-tumor heterogeneities. Biology-inspired mathematical models are capable of attacking this problem, but tumor heterogeneity is often overlooked in in-vivo modeling studies, while phenotypic considerations capturing spatial dynamics are not typically included in in-vitro modeling studies. We present a data assimilation-prediction pipeline with a two-phenotype model that includes a spatiotemporal component to characterize and predict the evolution of in-vitro breast cancer cells and their heterogeneous response to chemotherapy. Our model assumes that the cells can be divided into two subpopulations: surviving cells unaffected by the treatment, and irreversibly damaged cells undergoing treatment-induced death. MCF7 breast cancer cells were previously cultivated in wells for up to 1000 hours, treated with various concentrations of doxorubicin and imaged with time-resolved microscopy to record spatiotemporally-resolved cell count data. Images were used to generate cell density maps. Treatment response predictions were initialized by a training set and updated by weekly measurements. Our mathematical model successfully calibrated the spatiotemporal cell growth dynamics, achieving median [range] concordance correlation coefficients of > .99 [.88, >.99] and .73 [.58, .85] across the whole well and individual pixels, respectively. Our proposed data assimilation-prediction approach achieved values of .97 [.44, >.99] and .69 [.35, .79] for the whole well and individual pixels, respectively. Thus, our model can capture and predict the spatiotemporal dynamics of MCF7 cells treated with doxorubicin in an in-vitro setting.

Ceftolozane-Tazobactam against Pseudomonas aeruginosa Cystic Fibrosis Clinical Isolates in the Hollow-Fiber Infection Model: Challenges Imposed by Hypermutability and Heteroresistance

Pseudomonas aeruginosa remains a challenge in chronic respiratory infections in cystic fibrosis (CF). Ceftolozane-tazobactam has not yet been evaluated against multidrug-resistant hypermutable P. aeruginosa isolates in the hollow-fiber infection model (HFIM). Isolates CW41, CW35, and CW44 (ceftolozane-tazobactam MICs of 4, 4, and 2 mg/L, respectively) from adults with CF were exposed to simulated representative epithelial lining fluid pharmacokinetics of ceftolozane-tazobactam in the HFIM. Regimens were continuous infusion (CI; 4.5 g/day to 9 g/day, all isolates) and 1-h infusions (1.5 g every 8 hours and 3 g every 8 hours, CW41). Whole-genome sequencing and mechanism-based modeling were performed for CW41. CW41 (in four of five biological replicates) and CW44 harbored preexisting resistant subpopulations; CW35 did not. For replicates 1 to 4 of CW41 and CW44, 9 g/day CI decreased bacterial counts to <3 log10 CFU/mL for 24 to 48 h, followed by regrowth and resistance amplification. Replicate 5 of CW41 had no preexisting subpopulations and was suppressed below ~3 log10 CFU/mL for 120 h by 9 g/day CI, followed by resistant regrowth. Both CI regimens reduced CW35 bacterial counts to <1 log10 CFU/mL by 120 h without regrowth. These results corresponded with the presence or absence of preexisting resistant subpopulations and resistance-associated mutations at baseline. Mutations in ampC, algO, and mexY were identified following CW41 exposure to ceftolozane-tazobactam at 167 to 215 h. Mechanism-based modeling well described total and resistant bacterial counts. The findings highlight the impact of heteroresistance and baseline mutations on the effect of ceftolozane-tazobactam and limitations of MIC to predict bacterial outcomes. The resistance amplification in two of three isolates supports current guidelines that ceftolozane-tazobactam should be utilized together with another antibiotic against P. aeruginosa in CF.

Pharmacodynamic Drug-Drug Interactions

Pharmacodynamic drug-drug interactions (DDIs) occur when the pharmacological effect of one drug is altered by that of another drug in a combination regimen. DDIs often are classified as synergistic, additive, or antagonistic in nature, albeit these terms are frequently misused. Within a complex pathophysiological system, the mechanism of interaction may occur at the same target or through alternate pathways. Quantitative evaluation of pharmacodynamic DDIs by employing modeling and simulation approaches is needed to identify and optimize safe and effective combination therapy regimens. This review investigates the opportunities and challenges in pharmacodynamic DDI studies and highlights examples of quantitative methods for evaluating pharmacodynamic DDIs, with a particular emphasis on the use of mechanism-based modeling and simulation in DDI studies. Advancements in both experimental and computational techniques will enable the application of better, model-informed assessments of pharmacodynamic DDIs in drug discovery, development, and therapeutics.

Meropenem Combined with Ciprofloxacin Combats Hypermutable Pseudomonas aeruginosa from Respiratory Infections of Cystic Fibrosis Patients

Hypermutable Pseudomonas aeruginosa organisms are prevalent in chronic respiratory infections and have been associated with reduced lung function in cystic fibrosis (CF); these isolates can become resistant to all antibiotics in monotherapy. This study aimed to evaluate the time course of bacterial killing and resistance of meropenem and ciprofloxacin in combination against hypermutable and nonhypermutable P. aeruginosa Static concentration time-kill experiments over 72 h assessed meropenem and ciprofloxacin in mono- and combination therapies against PAO1 (nonhypermutable), PAOΔmutS (hypermutable), and hypermutable isolates CW8, CW35, and CW44 obtained from CF patients with chronic respiratory infections. Meropenem (1 or 2 g every 8 h [q8h] as 3-h infusions and 3 g/day as a continuous infusion) and ciprofloxacin (400 mg q8h as 1-h infusions) in monotherapies and combinations were further evaluated in an 8-day hollow-fiber infection model study (HFIM) against CW44. Concentration-time profiles in lung epithelial lining fluid reflecting the pharmacokinetics in CF patients were simulated and counts of total and resistant bacteria determined. All data were analyzed by mechanism-based modeling (MBM). In the HFIM, all monotherapies resulted in rapid regrowth with resistance at 48 h. The maximum daily doses of 6 g meropenem (T_>MIC of 80% to 88%) and 1.2 g ciprofloxacin (area under the concentration-time curve over 24 h in the steady state divided by the MIC [AUC/MIC], 176), both given intermittently, in monotherapy failed to suppress regrowth and resulted in substantial emergence of resistance (≥7.6 log₁₀ CFU/ml resistant populations). The combination of these regimens achieved synergistic killing and suppressed resistance. MBM with subpopulation and mechanistic synergy yielded unbiased and precise curve fits. Thus, the combination of 6 g/day meropenem plus ciprofloxacin holds promise for future clinical evaluation against infections by susceptible hypermutable P. aeruginosa.

Mechanism-Based Pharmacokinetic/Pharmacodynamic Modeling of Aerosolized Colistin in a Mouse Lung Infection Model

Optimized dosage regimens of aerosolized colistin (as colistin methanesulfonate [CMS]) are urgently required to maximize bacterial killing against multidrug-resistant Gram-negative bacteria while minimizing toxicity. This study aimed to develop a mechanism-based pharmacokinetic (PK)/pharmacodynamic (PD) model (MBM) for aerosolized colistin based upon PK/PD data in neutropenic infected mice and to perform a deterministic simulation with the PK of aerosolized colistin (as CMS) in critically ill patients. In vivo time-kill experiments were carried out with three different strains of Pseudomonas aeruginosa An MBM was developed in S-ADAPT and evaluated by assessing its ability to predict the PK/PD index associated with efficacy in mice. A deterministic simulation with human PK data was undertaken to predict the efficacy of current dosage regimens of aerosolized colistin in critically ill patients. In the final MBM, the total bacterial population for each isolate consisted of colistin-susceptible and -resistant subpopulations. The antimicrobial efficacy of aerosolized colistin was best described by a sigmoidal E_max model whereby colistin enhanced the rate of bacterial death. Deterministic simulation with human PK data predicted that an inhalational dosage regimen of 60 mg colistin base activity (CBA) every 12 h is needed to achieve a ≥2-log₁₀ bacterial reduction (as the number of CFU per lung) in critically ill patients at 24 h after commencement of inhaled therapy. In conclusion, the developed MBM is a useful tool for optimizing inhalational dosage regimens of colistin. Clinical studies are warranted to validate and refine our MBM for aerosolized colistin.

Zika Virus Replication Is Substantially Inhibited by Novel Favipiravir and Interferon Alpha Combination Regimens

Zika virus (ZIKV) is a major public health concern due to its overwhelming spread into the Americas. Currently, there are neither licensed vaccines nor antiviral therapies available for the treatment of ZIKV. We aimed to identify and rationally optimize effective therapeutic regimens for ZIKV by evaluating the antiviral potentials of the approved broad-spectrum antiviral agents favipiravir (FAV), interferon alpha (IFN), and ribavirin (RBV) as single agents and in combinations. For these studies, Vero cells were infected with ZIKV in the presence of increasing concentrations of FAV, IFN, or/and RBV for 4 days. Supernatants were harvested daily, and the viral burden was quantified by a plaque assay on Vero cells. The time course of the viral burden during treatment in vitro was characterized by a novel translational, mechanism-based model, which was subsequently used to rationally optimize combination dosage regimens. The combination regimen of FAV plus IFN provided the greatest extent of viral inhibition without cytotoxicity, reducing the viral burden by 4.4 log₁₀ PFU/ml at concentrations of 250 μM FAV and 100 IU/ml IFN. Importantly, these concentrations are achievable in humans. The translational, mechanism-based model yielded unbiased and reasonably precise curve fits. Simulations with the model predicted that clinically relevant regimens of FAV plus IFN would markedly reduce viral burdens in humans, resulting in at least a 10,000-fold reduction in the amount of the virus during the first 4 days of treatment. These findings highlight the substantial promise of rationally optimized combination dosage regimens of FAV plus IFN, which should be further investigated to combat ZIKV.

Influence of rhlR and lasR on Polymyxin Pharmacodynamics in Pseudomonas aeruginosa and Implications for Quorum Sensing Inhibition with Azithromycin

The impact of quorum sensing on polymyxin and azithromycin pharmacodynamics was assessed in Pseudomonas aeruginosa PAO1 and an isogenic rhlR/lasR double knockout. For polymyxin B, greater killing against the rhlR/lasR knockout than against PAO1 was observed at 10⁸ CFU/ml (polymyxin B half-maximal effective concentration [EC₅₀], 5.61 versus 12.5 mg/liter, respectively; P < 0.005). Polymyxin B combined with azithromycin (256 mg/liter) was synergistic against each strain, significantly reducing the respective polymyxin B EC₅₀ compared to those with monotherapy (P < 0.005), and is a promising strategy by which to combat P. aeruginosa.

Mathematical modeling of plant allelopathic hormesis based on ecological-limiting-factor models

Allelopathy arises from the release of chemicals by one plant species that affect other species in its vicinity, usually to their detriment. Allelopathic effects have been demonstrated to be limiting factors for species distributions and ecological processes in some natural or agricultural communities. Based on the biphasic hormetic responses of plants to allelochemicals, ecological-limiting-factor models were introduced into the An-Johnson-Lovett hormesis model to improve modelling the phenomenon of allelopathic hormesis and to better reflect the nature of allelopathy as a limiting factor in ecological processes. Outcomes of the models have been compared for several sets of experimental data from the literature and good agreement between the models and data was observed, which indicates that the new models give some insight into the ecological mechanisms involved and may provide more options for modelling the allelopathic phenomenon as well as platforms for further research on plant allelopathic hormesis.