Skin pharmacokinetics of miltefosine in the treatment of post-kala-azar dermal leishmaniasis in South Asia.

INTRODUCTION: Post-kala-azar dermal leishmaniasis (PKDL) arises as a dermal complication following a visceral leishmaniasis (VL) infection. Current treatment options for PKDL are unsatisfactory, and there is a knowledge gap regarding the distribution of antileishmanial compounds within human skin. The present study investigated the skin distribution of miltefosine in PKDL patients, with the aim to improve the understanding of the pharmacokinetics at the skin target site in PKDL. METHODS: Fifty-two PKDL patients underwent treatment with liposomal amphotericin B (20 mg/kg) plus miltefosine (allometric dosing) for 21 days. Plasma concentrations of miltefosine were measured on study days 8, 15, 22 and 30, while a punch skin biopsy was taken on day 22. A physiologically based pharmacokinetic (PBPK) model was developed to evaluate the distribution of miltefosine into the skin. RESULTS: Following the allometric weight-based dosing regimen, median miltefosine concentrations on day 22 were 43.73 µg/g (IQR: 21.94-60.65 µg/g) in skin and 33.29 µg/mL (IQR: 25.9-42.58 µg/mL) in plasma. The median individual concentration ratio of skin to plasma was 1.19 (IQR: 0.79-1.9). In 87% (45/52) of patients, skin exposure was above the suggested EC90 PK target of 10.6 mg/L associated with in vitro susceptibility. Simulations indicated that the residence time of miltefosine in the skin would be more than 2-fold longer than in plasma, estimated by a mean residence time of 604 versus 266 hours, respectively. CONCLUSION: This study provides the first accurate measurements of miltefosine penetration into the skin, demonstrating substantial exposure and prolonged retention of miltefosine within the skin. These findings support the use of miltefosine in cutaneous manifestations of leishmaniasis. In combination with parasitological and clinical data, these results are critical for the future optimization of combination therapies with miltefosine in the treatment of PKDL.

Impact of new direct-acting antiviral therapy on the prevalence and undiagnosed proportion of chronic hepatitis C infection.

BACKGROUND: Patients with chronic hepatitis C (CHC) can be cured with the new highly effective interferon-free combination treatments (DAA) that were approved in 2014. However, CHC is a largely silent disease, and many individuals are unaware of their infections until the late stages of the disease. The impact of wider access to effective treatments and improved awareness of the disease on the number of infections and the number of patients who remain undiagnosed is not known in Canada. Such evidence can guide the development of strategies and interventions to reduce the burden of CHC and meet World Health Organization's (WHO) 2030 elimination targets. The purpose of this study is to use a back-calculation framework informed by provincial population-level health administrative data to estimate the prevalence of CHC and the proportion of cases that remain undiagnosed in the three most populated provinces in Canada: British Columbia (BC), Ontario and Quebec. METHODS: We have conducted a population-based retrospective analysis of health administrative data for the three provinces to generate the annual incidence of newly diagnosed CHC cases, decompensated cirrhosis (DC), hepatocellular carcinoma (HCC) and HCV treatment initiations. For each province, the data were stratified in three birth cohorts: individuals born prior to 1945, individuals born between 1945 and 1965 and individuals born after 1965. We used a back-calculation modelling approach to estimate prevalence and the undiagnosed proportion of CHC. The historical prevalence of CHC was inferred through a calibration process based on a Bayesian Markov chain Monte Carlo (MCMC) algorithm. The algorithm constructs the historical prevalence of CHC for each cohort by comparing the model-generated outcomes of the annual incidence of the CHC-related health events against the data set of observed diagnosed cases generated in the retrospective analysis. RESULTS: The results show a decreasing trend in both CHC prevalence and undiagnosed proportion in BC, Ontario and Quebec. In 2018, CHC prevalence was estimated to be 1.23% (95% CI: .96%-1.62%), .91% (95% CI: .82%-1.04%) and .57% (95% CI: .51%-.64%) in BC, Ontario and Quebec respectively. The CHC undiagnosed proportion was assessed to be 35.44% (95% CI: 27.07%-45.83%), 34.28% (95% CI: 26.74%-41.62%) and 46.32% (95% CI: 37.85%-52.80%) in BC, Ontario and Quebec, respectively, in 2018. Also, since the introduction of new DAA treatment in 2014, CHC prevalence decreased from 1.39% to 1.23%, .97% to .91% and .65% to .57% in BC, Ontario and Quebec respectively. Similarly, the CHC undiagnosed proportion decreased from 38.78% to 35.44%, 38.70% to 34.28% and 47.54% to 46.32% in BC, Ontario and Quebec, respectively, from 2014 to 2018. CONCLUSIONS: We estimated that the CHC prevalence and undiagnosed proportion have declined for all three provinces since the new DAA treatment has been approved in 2014. Yet, our findings show that a significant proportion of HCV cases remain undiagnosed across all provinces highlighting the need to increase investment in screening. Our findings provide essential evidence to guide decisions about current and future HCV strategies and help achieve the WHO goal of eliminating hepatitis C in Canada by 2030.

Mechanistic Skin Modeling of Plasma Concentrations of Sunscreen Active Ingredients Following Facial Application.

Sunscreen products constitute two distinct categories. Recreational sunscreens protect against high-intensity, episodic sun exposure, often applied over the entire body. In contrast, facial sunscreen products are designed for sub-erythemal, low-intensity daily sun exposure. Such different exposures necessitate distinctive product safety assessments. Building on earlier methods for predicting dermal disposition, a mechanistic model was developed to simulate plasma concentrations of seven organic sunscreen active ingredients: avobenzone, ensulizole, homosalate, octinoxate, octisalate, octocrylene, and oxybenzone, following facial application. In vitro permeation testing (IVPT) was performed with two different vehicles using a subset of the UV filters. These IVPT results, in addition to previously published IVPT data and published in vivo Maximal Usage Trial (MUsT) data for the UV filters, were used to train the mechanistic dermal model via a Bayesian Markov chain Monte Carlo (MCMC) method. An external validation of the trained model with real-world in vivo datasets demonstrated that the model's predicted UV filter plasma concentrations align well with experimental measurements and capture the observed inter-individual variability. Predictions of steady-state UV filter plasma concentrations under facial application scenarios at 5% concentration and at the maximal allowable concentrations were then generated by the trained model. Oxybenzone had the greatest predicted plasma concentration following facial application. Homosalate and octisalate predictions had high uncertainty associated with the absence of data. Several application scenarios pertaining to avobenzone, ensulizole, octocrylene and octinoxate were identified in which median plasma concentration levels were at 0.5 ng/ml or below when applied in the recreational or facial product. Model limitations include uncertainty in vehicle/water partitioning, formulation metamorphosis, and UV filter systemic clearance, all of which can be refined with additional data. For UV filters, limiting exposure to facial application reduces human safety concerns based on FDA established thresholds.

Enhancement of Skin Permeability Prediction through PBPK Modeling, Bayesian Inference, and Experiment Design.

Physiologically based pharmacokinetic (PBPK) models of skin absorption are a powerful resource for estimating drug delivery and chemical risk of dermatological products. This paper presents a PBPK workflow for the quantification of the mechanistic determinants of skin permeability and the use of these quantities in the prediction of skin absorption in novel contexts. A state-of-the-art mechanistic model of dermal absorption was programmed into an open-source modeling framework. A sensitivity analysis was performed to identify the uncertain compound-specific, individual-specific, and site-specific model parameters that impact permeability. A Bayesian Markov Chain Monte Carlo algorithm was employed to derive distributions of these parameters given in vitro experimental permeability measurements. Extrapolations to novel contexts were generated by simulating the model following its update with samples drawn from the learned distributions as well as parameters that represent the intended scenario. This algorithm was applied multiple times, each using a unique set of permeability measurements sourced under experimental contexts that differ in terms of the compound, vehicle pH, skin sample anatomical site, and the number of compounds under which each subject's skin samples were tested. Among the data sets used in this study, the highest accuracy and precision in the extrapolated permeability was achieved in those that include measurements conducted under multiple vehicle pH levels and in which individual subjects' skin samples are tested under multiple compounds. This work thus identifies factors for consideration in the design of experiments for the purpose of training dermal models to robustly estimate drug delivery and chemical risk.

Cannabidiol Exposure Through Maternal Marijuana Use: Predictions in Breastfed Infants.

BACKGROUND AND OBJECTIVE: Knowledge about exposure to cannabidiol (CBD) in breastfed infants can provide an improved understanding of potential risk. The aim was to predict CBD exposure in breastfed infants from mothers taking CBD and CBD-containing products. METHODS: Cannabidiol concentrations in milk previously attained from data collected through an existing human milk research biorepository were used to simulate infant doses and identify subgroups. A developed pediatric physiologically based pharmacokinetic model produced virtual breastfed infants administered the simulated CBD doses. Predicted breastfed infant exposures and upper area under the curve ratios were compared to the lowest therapeutic dose for approved indications in children. RESULTS: The existing human milk research biorepository contained 200 samples from 181 unique breastfeeding mothers for whom self-reported administration data and CBD concentrations had previously been measured. Samples that were above the lower limit of quantification with only one maternal administration type revealed that administration type, i.e., joint/blunt or edible versus oil or pipe, resulted in significantly different subgroups in terms of milk concentrations. Resulting simulated infant doses (ng/kg) were described by lognormal distributions with geometric means and geometric standard deviations: 0.61 ± 2.41 all concentrations, 0.10 ± 0.37 joint/blunt or edible, and 2.23 ± 8.15 oil or pipe. Doses administered to breastfed infants had exposures magnitudes lower than exposures in children aged 4-11 years administered the lowest therapeutic dose for approved indications, and low upper area under the curve ratios. CONCLUSIONS: Based on real-world use, breastfeeding infants are predicted to receive very small exposures of CBD through milk. Studies examining adverse reactions will provide further insight into potential risk.

A province-by-province cost-effectiveness analysis and budget impact analysis of one-time birth cohort screening of hepatitis C virus (HCV) infection in Canada.

Managing chronic hepatitis C is challenging, as the majority of those infected are asymptomatic. Therefore, to ensure treatments are administered before the onset of severe complications, screening is important. In Canada, uncertainty regarding the cost-effectiveness and budget impact of screening has led to conflicting recommendations. The objective of this study is to estimate the cost-effectiveness and budget-impact of one-time HCV screening. A state-transition model was developed to evaluate the cost-effectiveness and budget-impact between a risk-based screening strategy (current-practice) and a one-time screening strategy on three different birth-cohorts. Cost and prevalence data were obtained from administrative data. Progression and utility data were based on recent systematic reviews. We used a provincial payer-perspective, life-time time-horizon and a 1.5% discount rate for the cost-effectiveness analysis, and used a 10-year time-horizon and no discounting for the budget-impact analysis. One-time screening strategy would cost more and provide more health benefits than the risk-based screening for all birth cohorts. For those born after 1964, the incremental-cost-effectiveness-ratio (ICER) per quality-adjusted-life-year (QALY) of screening versus current-practice varied from $27,422/QALY to $42,191/QALY across different provinces. One-time screening of the cohort would cost an additional $2 million to $236 million across different provinces. For those born 1945-1964, the ICER of screening versus current-practice varied from $35,217/QALY to $48,197/QALY across different provinces. For the cohort born before 1945, the ICER of screening versus current-practice was not cost-effective at a willingness-to-pay threshold of $50,000/QALY across all provinces. Our cost-effectiveness analysis suggests that a one-time HCV screening program for those born after 1945 is cost-effective. Considering the budget impact relative to other funded recommended health services and technologies, HCV screening could be considered affordable.

Model-Based Assessment of the Contribution of Monocytes and Macrophages to the Pharmacokinetics of Monoclonal Antibodies.

PURPOSE: We have hypothesized that a high concentration of circulating monocytes and macrophages may contribute to the fast weight-based clearance of monoclonal antibodies (mAbs) in young children. Exploring this hypothesis, this work uses modeling to clarify the role of monocytes and macrophages in the elimination of mAbs. METHODS: Leveraging pre-clinical data from mice, a minimal physiologically-based pharmacokinetic model was developed to characterize mAb uptake and FcRn-mediated recycling in circulating monocytes, macrophages, and endothelial cells. The model characterized IgG disposition in complex scenarios of site-specific FcRn deletion and variable endogenous IgG levels. Evaluation was performed for predicting IgG disposition with co-administration of high dose IVIG. A one-at-a-time sensitivity analysis quantified the role of relevant cellular parameters on IgG elimination in various scenarios. RESULTS: The plasma AUC of mAbs was highly sensitive to endothelial cell parameters, but had near-nil sensitivity to monocyte and macrophage parameters, even in scenarios with 90% loss of FcRn expression/activity. In mice with normal FcRn expression, simulations suggest that less than 2% of an IV dose is eliminated in macrophages, while endothelial cells are predicted to dominate mAb elimination. CONCLUSIONS: The model suggests that the role of monocytes and macrophages in IgG homeostasis includes extensive uptake and highly efficient FcRn-mediated protection, but not appreciable degradation when FcRn is present. Therefore, it is very unlikely that a high concentration of circulating monocytes can contribute to explaining the fast weight-based clearance of mAbs in very young children, even if FcRn expression/activity was 90% lower in children than in adults.

Development and Evaluation of an In Silico Dermal Absorption Model Relevant for Children.

The higher skin surface area to body weight ratio in children and the prematurity of skin in neonates may lead to higher chemical exposure as compared to adults. The objectives of this study were: (i) to provide a comprehensive review of the age-dependent anatomical and physiological changes in pediatric skin, and (ii) to construct and evaluate an age-dependent pediatric dermal absorption model. A comprehensive review was conducted to gather data quantifying the differences in the anatomy and physiology of child and adult skin. Maturation functions were developed for model parameters that were found to be age-dependent. A pediatric dermal absorption model was constructed by updating a MoBi implementation of the Dancik et al. 2013 skin permeation model with these maturation functions. Using a workflow for adult-to-child model extrapolation, the predictive performance of the model was evaluated by comparing its predicted rates of flux of diamorphine, phenobarbital and buprenorphine against experimental observations using neonatal skin. For diamorphine and phenobarbital, the model provided reasonable predictions. The ratios of predicted:observed flux in neonates for diamorphine ranged from 0.55 to 1.40. For phenobarbital, the ratios ranged from 0.93 to 1.26. For buprenorphine, the model showed acceptable predictive performance. Overall, the physiologically based pediatric dermal absorption model demonstrated satisfactory prediction accuracy. The prediction of dermal absorption in neonates using a model-based approach will be useful for both drug development and human health risk assessment.

A Mechanistic Bayesian Inferential Workflow for Estimation of In Vivo Skin Permeation from In Vitro Measurements.

Computational models can play an integral role in the chemical risk assessment of dermatological products. However, a limitation on the ability of mathematical models to extrapolate from in vitro measurements to in human predictions arises from context-dependence: modeling assumptions made in one setting may not carry over to another scenario. Mechanistic models of dermal absorption relate the skin penetration kinetics of permeants to their partitioning and diffusion across elementary sub-compartments of the skin. This endows them with a flexibility through which specific model components can be adjusted to better reflect dermal absorption in contexts that differ from the in vitro setting, while keeping fixed any context-invariant parameters that remain unchanged in the two scenarios. This paper presents a workflow for predicting in vivo dermal absorption by integrating a mechanistic model of skin penetration with in vitro permeation test (IVPT) measurements. A Bayesian approach is adopted to infer a joint posterior distribution of context-invariant model parameters. By populating the model with samples of context-invariant parameters from this distribution and adjusting context-dependent parameters to suit the in vivo setting, simulations of the model yield estimates of the likely range of in vivo dermal absorption given the IVPT data. This workflow is applied to five compounds previously tested in vivo. In each case, the range of in vivo predictions encompassed the range observed experimentally. These studies demonstrate that the proposed workflow enables the derivation of mechanistically derived upper bounds on dermal absorption for the purposes of chemical risk assessment.

Estimation of COVID-19 Period Prevalence and the Undiagnosed Population in Canadian Provinces: Model-Based Analysis.

BACKGROUND: The development of a successful COVID-19 control strategy requires a thorough understanding of the trends in geographic and demographic distributions of disease burden. In terms of the estimation of the population prevalence, this includes the crucial process of unravelling the number of patients who remain undiagnosed. OBJECTIVE: This study estimates the period prevalence of COVID-19 between March 1, 2020, and November 30, 2020, and the proportion of the infected population that remained undiagnosed in the Canadian provinces of Quebec, Ontario, Alberta, and British Columbia. METHODS: A model-based mathematical framework based on a disease progression and transmission model was developed to estimate the historical prevalence of COVID-19 using provincial-level statistics reporting seroprevalence, diagnoses, and deaths resulting from COVID-19. The framework was applied to three different age cohorts (< 30; 30-69; and ≥70 years) in each of the provinces studied. RESULTS: The estimates of COVID-19 period prevalence between March 1, 2020, and November 30, 2020, were 4.73% (95% CI 4.42%-4.99%) for Quebec, 2.88% (95% CI 2.75%-3.02%) for Ontario, 3.27% (95% CI 2.72%-3.70%) for Alberta, and 2.95% (95% CI 2.77%-3.15%) for British Columbia. Among the cohorts considered in this study, the estimated total number of infections ranged from 2-fold the number of diagnoses (among Quebecers, aged ≥70 years: 26,476/53,549, 49.44%) to 6-fold the number of diagnoses (among British Columbians aged ≥70 years: 3108/18,147, 17.12%). CONCLUSIONS: Our estimates indicate that a high proportion of the population infected between March 1 and November 30, 2020, remained undiagnosed. Knowledge of COVID-19 period prevalence and the undiagnosed population can provide vital evidence that policy makers can consider when planning COVID-19 control interventions and vaccination programs.

Assessment of Vehicle Volatility and Deposition Layer Thickness in Skin Penetration Models.

Systemic disposition of dermally applied chemicals is often formulation-dependent. Rapid evaporation of the vehicle can result in crystallization of active compounds, limiting their degree of skin penetration. In addition, the choice of vehicle can affect the permeant's degree of penetration into the stratum corneum. The aim of this study is to build a predictive, mechanistic, dermal absorption model that accounts for vehicle-specific effects on the kinetics of permeant transport into skin. An existing skin penetration model is extended to explicitly include the effect of vehicle volatility over time. Using in vitro measurements of skin penetration by chemicals applied in both a saline and an ethanol solvent, the model is optimized to learn two vehicle-specific quantities: the solvent evaporation rate and the extent of permeant deposition into the upper stratum corneum immediately following application. The dermal disposition estimates of the trained model are subsequently compared against those of the original model using further in vitro measurements. The trained model showed a 1.5-fold improvement and a 19-fold improvement in overall goodness of fit among compounds tested in saline and ethanol solvents, respectively. The proposed model structure can thus form a basis for in vitro to in vivo extrapolations of dermal disposition for skin formulations containing volatile components.

Cost-effectiveness analysis of sofosbuvir and velpatasvir in chronic hepatitis C patients with decompensated cirrhosis.

BACKGROUND: Current literature indicates that direct-acting antivirals (DAAs) are cost-effective to treat compensated cirrhotic patients with hepatitis C. Although already funded by public payers, it is unknown whether it is economical to reimburse DAAs within the more advanced decompensated cirrhosis population. METHODS: A state-transition model was developed to conduct a cost-utility analysis of sofosbuvir-velpatasvir (SOF/VEL) plus ribavirin regimen for 12 weeks. The evaluated cohort had a mean age of 58 years and Child-Turcotte-Pugh (CTP) class B cirrhosis with decompensated symptoms. A scenario analysis was performed on CTP C patients. We used a payer perspective, a lifetime time horizon and a 1.5% annual discount rate. RESULTS: While SOF/VEL plus ribavirin treatment for 12 weeks increased costs by $156 676, it provided an extra 4.00 quality-adjusted life years (QALYs) compared to best supportive care (no DAA therapy). With an incremental cost-effectiveness ratio of $39 169 per QALY, SOF/VEL plus ribavirin was determined to be cost-effective at a willingness to pay of $50 000 per QALY. SOF/VEL reduced liver-related deaths and reduced progression to CTP C cirrhosis by 20.4% and 21.9%, respectively. On the contrary, SOF/VEL regimen resulted in increases in liver transplants and hepatocellular carcinoma (HCC) by 54.0% and 42.5%, respectively. Similar results were found for CTP C patients. CONCLUSION: This analysis informs payers that SOF/VEL should continue to be reimbursed in decompensated hepatitis C patients. It also supports the recommendations by the American Association for the Study of Liver Diseases to continue screening for HCC in decompensated cirrhotic patients who have achieved sustained virologic response.

Estimating chronic hepatitis C prevalence in British Columbia and Ontario, Canada, using population-based cohort studies.

Patients identified as having chronic hepatitis C (CHC) infection can be effectively and rapidly treated using direct-acting antiviral agents. However, there remains a substantial burden of subclinical undetected infection. This study estimates the prevalence and undiagnosed proportion of CHC in British Columbia (BC) and Ontario, Canada, using a model-based approach, informed by provincial population-level health administrative data. A two-step approach was used: Step 1) Two population-based retrospective analyses of administrative health data for a cohort of British Columbians and a cohort of Ontarians with CHC were conducted to generate population-level statistics of CHC-related health events; Step 2) using a validated natural history model of hepatitis C virus (HCV) infection, the historical prevalence of CHC was back-calculated from the data collected in Step 1. Our retrospective study found that, in BC and Ontario, the number of newly diagnosed CHC cases is declining yearly while the complications of the disease are increasing yearly. BC had a 2014 CHC prevalence of 1.04% (95% CI: 0.84%-1.44%), with 33.3% (95% CI: 25.5%-42.0%) of CHC cases undiagnosed. Ontario had a 2014 CHC prevalence of 0.91% (95% CI: 0.83%-1.02%) with 36.0% (95% CI: 31.2%-38.9%) of CHC cases undiagnosed. Our study offers robust estimates based on the integration of a validated natural history model with population-level health administrative data on HCV-related events, which can provide vital evidence for policymakers to develop appropriate policies to achieve elimination targets. Our approach can also be applied to produce robust region-specific estimates in other countries.

A model-based framework for chronic hepatitis C prevalence estimation.

Chronic hepatitis C (CHC) continues to be a highly burdensome disease worldwide. The often-asymptomatic nature of early-stage CHC means that the disease often remains undiagnosed, leaving its prevalence highly uncertain. This generates significant uncertainty in the planning of hepatitis C eradication programs to meet WHO targets. The aim of this work is to establish a mathematical framework for the estimation of a geographic locale's CHC prevalence and the proportion of its CHC population that remains undiagnosed. A Bayesian MCMC approach is taken to infer these populations from the observed occurrence of CHC-related events using a recently published natural history model of the disease. Using the Canadian context as a specific example, this study estimates that in 2013, the CHC prevalence rate in Canada was 0.63% (95% CI: 0.53% - 0.72%), with 27.1% (95% CI: 19.3% - 36.1%) of the infected population undiagnosed.

Population PBPK modelling of trastuzumab: a framework for quantifying and predicting inter-individual variability.

In this work we proposed a population physiologically-based pharmacokinetic (popPBPK) framework for quantifying and predicting inter-individual pharmacokinetic variability using the anti-HER2 monoclonal antibody (mAb) trastuzumab as an example. First, a PBPK model was developed to account for the possible mechanistic sources of variability. Within the model, five key factors that contribute to variability were identified and the nature of their contribution was quantified with local and global sensitivity analyses. The five key factors were the concentration of membrane-bound HER2 ([Formula: see text]), the convective flow rate of mAb through vascular pores ([Formula: see text]), the endocytic transport rate of mAb through vascular endothelium ([Formula: see text]), the degradation rate of mAb-HER2 complexes ([Formula: see text]) and the concentration of shed HER2 extracellular domain in circulation ([Formula: see text]). [Formula: see text] was the most important parameter governing trastuzumab distribution into tissues and primarily affected variability in the first 500 h post-administration. [Formula: see text] was the most significant contributor to variability in clearance. These findings were used together with population generation methods to accurately predict the observed variability in four experimental trials with trastuzumab. To explore anthropometric sources of variability, virtual populations were created to represent participants in the four experimental trials. Using populations with only their expected anthropometric diversity resulted in under-prediction of the observed inter-individual variability. Adapting the populations to include literature-based variability around the five key parameters enabled accurate predictions of the variability in the four trials. The successful application of this framework demonstrates the utility of popPBPK methods to understand the mechanistic underpinnings of pharmacokinetic variability.

Transient dynamic phenotypes as criteria for model discrimination: fold-change detection in Rhodobacter sphaeroides chemotaxis.

The chemotaxis pathway of the bacterium Rhodobacter sphaeroides shares many similarities with that of Escherichia coli. It exhibits robust adaptation and has several homologues of the latter's chemotaxis proteins. Recent theoretical results have correctly predicted that the E. coli output behaviour is unchanged under scaling of its ligand input signal; this property is known as fold-change detection (FCD). In the light of recent experimental results suggesting that R. sphaeroides may also show FCD, we present theoretical assumptions on the R. sphaeroides chemosensory dynamics that can be shown to yield FCD behaviour. Furthermore, it is shown that these assumptions make FCD a property of this system that is robust to structural and parametric variations in the chemotaxis pathway, in agreement with experimental results. We construct and examine models of the full chemotaxis pathway that satisfy these assumptions and reproduce experimental time-series data from earlier studies. We then propose experiments in which models satisfying our theoretical assumptions predict robust FCD behaviour where earlier models do not. In this way, we illustrate how transient dynamic phenotypes such as FCD can be used for the purposes of discriminating between models that reproduce the same experimental time-series data.

Feedback control architecture and the bacterial chemotaxis network.

Bacteria move towards favourable and away from toxic environments by changing their swimming pattern. This response is regulated by the chemotaxis signalling pathway, which has an important feature: it uses feedback to 'reset' (adapt) the bacterial sensing ability, which allows the bacteria to sense a range of background environmental changes. The role of this feedback has been studied extensively in the simple chemotaxis pathway of Escherichia coli. However it has been recently found that the majority of bacteria have multiple chemotaxis homologues of the E. coli proteins, resulting in more complex pathways. In this paper we investigate the configuration and role of feedback in Rhodobacter sphaeroides, a bacterium containing multiple homologues of the chemotaxis proteins found in E. coli. Multiple proteins could produce different possible feedback configurations, each having different chemotactic performance qualities and levels of robustness to variations and uncertainties in biological parameters and to intracellular noise. We develop four models corresponding to different feedback configurations. Using a series of carefully designed experiments we discriminate between these models and invalidate three of them. When these models are examined in terms of robustness to noise and parametric uncertainties, we find that the non-invalidated model is superior to the others. Moreover, it has a 'cascade control' feedback architecture which is used extensively in engineering to improve system performance, including robustness. Given that the majority of bacteria are known to have multiple chemotaxis pathways, in this paper we show that some feedback architectures allow them to have better performance than others. In particular, cascade control may be an important feature in achieving robust functionality in more complex signalling pathways and in improving their performance.

A model invalidation-based approach for elucidating biological signalling pathways, applied to the chemotaxis pathway in R. sphaeroides.

BACKGROUND: Developing methods for understanding the connectivity of signalling pathways is a major challenge in biological research. For this purpose, mathematical models are routinely developed based on experimental observations, which also allow the prediction of the system behaviour under different experimental conditions. Often, however, the same experimental data can be represented by several competing network models. RESULTS: In this paper, we developed a novel mathematical model/experiment design cycle to help determine the probable network connectivity by iteratively invalidating models corresponding to competing signalling pathways. To do this, we systematically design experiments in silico that discriminate best between models of the competing signalling pathways. The method determines the inputs and parameter perturbations that will differentiate best between model outputs, corresponding to what can be measured/observed experimentally. We applied our method to the unknown connectivities in the chemotaxis pathway of the bacterium Rhodobacter sphaeroides. We first developed several models of R. sphaeroides chemotaxis corresponding to different signalling networks, all of which are biologically plausible. Parameters in these models were fitted so that they all represented wild type data equally well. The models were then compared to current mutant data and some were invalidated. To discriminate between the remaining models we used ideas from control systems theory to determine efficiently in silico an input profile that would result in the biggest difference in model outputs. However, when we applied this input to the models, we found it to be insufficient for discrimination in silico. Thus, to achieve better discrimination, we determined the best change in initial conditions (total protein concentrations) as well as the best change in the input profile. The designed experiments were then performed on live cells and the resulting data used to invalidate all but one of the remaining candidate models. CONCLUSION: We successfully applied our method to chemotaxis in R. sphaeroides and the results from the experiments designed using this methodology allowed us to invalidate all but one of the proposed network models. The methodology we present is general and can be applied to a range of other biological networks.