Evolving Improved Sampling Protocols for Dose-Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric.

Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3-20), and it reduced the parameter variance metric by 33-37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.

Asymptotic analysis of particle cluster formation in the presence of anchoring sites.

The aggregation or clustering of proteins and other macromolecules plays an important role in the formation of large-scale molecular assemblies within cell membranes. Examples of such assemblies include lipid rafts, and postsynaptic domains (PSDs) at excitatory and inhibitory synapses in neurons. PSDs are rich in scaffolding proteins that can transiently trap transmembrane neurotransmitter receptors, thus localizing them at specific spatial positions. Hence, PSDs play a key role in determining the strength of synaptic connections and their regulation during learning and memory. Recently, a two-dimensional (2D) diffusion-mediated aggregation model of PSD formation has been developed in which the spatial locations of the clusters are determined by a set of fixed anchoring sites. The system is kept out of equilibrium by the recycling of particles between the cell membrane and interior. This results in a stationary distribution consisting of multiple clusters, whose average size can be determined using an effective mean-field description of the particle concentration around each anchored cluster. In this paper, we derive corrections to the mean-field approximation by applying the theory of diffusion in singularly perturbed domains. The latter is a powerful analytical method for solving two-dimensional (2D) and three-dimensional (3D) diffusion problems in domains where small holes or perforations have been removed from the interior. Applications range from modeling intracellular diffusion, where interior holes could represent subcellular structures such as organelles or biological condensates, to tracking the spread of chemical pollutants or heat from localized sources. In this paper, we take the bounded domain to be the cell membrane and the holes to represent anchored clusters. The analysis proceeds by partitioning the membrane into a set of inner regions around each cluster, and an outer region where mean-field interactions occur. Asymptotically matching the inner and outer stationary solutions generates an asymptotic expansion of the particle concentration, which includes higher-order corrections to mean-field theory that depend on the positions of the clusters and the boundary of the domain. Motivated by a recent study of light-activated protein oligomerization in cells, we also develop the analogous theory for cluster formation in a three-dimensional (3D) domain. The details of the asymptotic analysis differ from the 2D case due to the contrasting singularity structure of 2D and 3D Green's functions.

Mechanics limits ecological diversity and promotes heterogeneity in confined bacterial communities.

Multispecies bacterial populations often inhabit confined and densely packed environments where spatial competition determines the ecological diversity of the community. However, the role of mechanical interactions in shaping the ecology is still poorly understood. Here, we study a model system consisting of two populations of nonmotile Escherichia coli bacteria competing within open, monolayer microchannels. The competitive dynamics is observed to be biphasic: After seeding, either one strain rapidly fixates or both strains orient into spatially stratified, stable communities. We find that mechanical interactions with other cells and local spatial constraints influence the resulting community ecology in unexpected ways, severely limiting the overall diversity of the communities while simultaneously allowing for the establishment of stable, heterogeneous populations of bacteria displaying disparate growth rates. Surprisingly, the populations have a high probability of coexisting even when one strain has a significant growth advantage. A more coccus morphology is shown to provide a selective advantage, but agent-based simulations indicate this is due to hydrodynamic and adhesion effects within the microchannel and not from breaking of the nematic ordering. Our observations are qualitatively reproduced by a simple Pólya urn model, which suggests the generality of our findings for confined population dynamics and highlights the importance of early colonization conditions on the resulting diversity and ecology of bacterial communities. These results provide fundamental insights into the determinants of community diversity in dense confined ecosystems where spatial exclusion is central to competition as in organized biofilms or intestinal crypts.

Tiger prowling: Distribution modelling for northward-expanding Aedes albopictus (Diptera: Culicidae) in Japan.

The Asian tiger mosquito, Aedes albopictus, is a significant public health concern owing to its expanding habitat and vector competence. Disease outbreaks attributed to this species have been reported in areas under its invasion, and its northward expansion in Japan has caused concern because of the potential for dengue virus infection in newly populated areas. Accurate prediction of Ae. albopictus distribution is crucial to prevent the spread of the disease. However, limited studies have focused on the prediction of Ae. albopictus distribution in Japan. Herein, we used the random forest model, a machine learning approach, to predict the current and potential future habitat ranges of Ae. albopictus in Japan. The model revealed that these mosquitoes prefer urban areas over forests in Japan on the current map. Under predictions for the future, the species will expand its range to the surrounding areas and eventually reach many areas of northeastern Kanto, Tohoku District, and Hokkaido, with a few variations in different scenarios. However, the affected human population is predicted to decrease owing to the declining birth rate. Anthropogenic and climatic factors contribute to range expansion, and urban size and population have profound impacts. This prediction map can guide responses to the introduction of this species in new areas, advance the spatial knowledge of diseases vectored by it, and mitigate the possible disease burden. To our knowledge, this is the first distribution-modelling prediction for Ae. albopictus with a focus on Japan.

How do host population dynamics impact Lyme disease risk dynamics in theoretical models?

Lyme disease is the most common wildlife-to-human transmitted disease reported in North America. The study of this disease requires an understanding of the ecology of the complex communities of ticks and host species involved in harboring and transmitting this disease. Much of the ecology of this system is well understood, such as the life cycle of ticks, and how hosts are able to support tick populations and serve as disease reservoirs, but there is much to be explored about how the population dynamics of different host species and communities impact disease risk to humans. In this study, we construct a stage-structured, empirically-informed model with host dynamics to investigate how host population dynamics can affect disease risk to humans. The model describes a tick population and a simplified community of three host species, where primary nymph host populations are made to fluctuate on an annual basis, as commonly observed in host populations. We tested the model under different environmental conditions to examine the effect of environment on the interactions of host dynamics and disease risk. Results show that allowing for host dynamics in the model reduces mean nymphal infection prevalence and increases the maximum annual prevalence of nymphal infection and the density of infected nymphs. Effects of host dynamics on disease measures of nymphal infection prevalence were nonlinear and patterns in the effect of dynamics on amplitude in nymphal infection prevalence varied across environmental conditions. These results highlight the importance of further study of the effect of community dynamics on disease risk. This will involve the construction of further theoretical models and collection of robust field data to inform these models. With a more complete understanding of disease dynamics we can begin to better determine how to predict and manage disease risk using these models.

Simulating worm feeding patterns with computational models.

Worms create complex paths when moving through sediment to feed. This research applies computer simulation models to provide a unique approach to visualise and quantify the process by which complex worm paths can emerge from simple local movement decisions. A grid environment is proposed in which worms can move with choice of up to 8 directions at each step. This uses a square grid with diagonal paths which has not been investigated before and the resulting number of complex paths is increased compared to triangular grids. Results identify many novel worm paths. Some of the resulting paths are symmetrical, others produce repetitive looping paths, others return to the origin. Interesting worm paths are identified with chaotic movement. Some include oscillating between chaotic and ordered movement for which the outcome is still unknown after millions of steps. A conclusion that may be extrapolated to other creatures is that local movement decisions of a species substantially determine the overall global search strategy that emerges.

Neuroretinal Cell Culture Model as a Tool for the Development of New Therapeutic Approaches for Oxidative Stress-Induced Ocular Diseases, with a Focus on Glaucoma.

Glaucoma is a heterogeneous group of optic neuropathies characterized by a progressive degeneration of the retinal ganglion cells (RGCs), leading to irreversible vision loss. Nowadays, the traditional therapeutic approach to glaucoma consists of lowering the intraocular pressure (IOP), which does not address the neurodegenerative features of the disease. Besides animal models of glaucoma, there is a considerable need for in vitro experimental models to propose new therapeutic strategies for this ocular disease. In this study, we elucidated the pathological mechanisms leading to neuroretinal R28 cell death after exposure to glutamate and hydrogen peroxide (H2O2) in order to develop new therapeutic approaches for oxidative stress-induced retinal diseases, including glaucoma. We were able to show that glutamate and H2O2 can induce a decrease in R28 cell viability in a concentration-dependent manner. A cell viability of about 42% was found after exposure to 3 mM of glutamate and about 56% after exposure to 100 µM of H2O2 (n = 4). Label-free quantitative mass spectrometry analysis revealed differential alterations of 193 and 311 proteins in R28 cells exposed to 3 mM of glutamate and 100 µM of H2O2, respectively (FDR < 1%; p < 0.05). Bioinformatics analysis indicated that the protein changes were associated with the dysregulation of signaling pathways, which was similar to those observed in glaucoma. Thus, the proteomic alteration induced by glutamate was associated with the inhibition of the PI3K/AKT signaling pathway. On the other hand, H2O2-induced toxicity in R28 cells was linked to the activation of apoptosis signaling and the inhibition of the mTOR and ERK/MAPK signaling pathways. Furthermore, the data show a similarity in the inhibition of the EIF2 and AMPK signaling pathways and the activation of the sumoylation and WNT/ß-catenin signaling pathways in both groups. Our findings suggest that the exposure of R28 cells to glutamate and H2O2 could induce glaucoma-like neurodegenerative features and potentially provide a suitable tool for the development of new therapeutic strategies for retinal diseases.

Population pharmacokinetic modelling of primaquine exposures in lactating women and breastfed infants.

Current guidelines advise against primaquine treatment for breastfeeding mothers to avoid the potential for haemolysis in infants with G6PD deficiency. To predict the haemolytic risk, the amount of drug received from the breast milk and the resulting infant drug exposure need to be characterised. Here, we develop a pharmacokinetic model to describe the drug concentrations in breastfeeding women using venous, capillary, and breast milk data. A mother-to-infant model is developed to mimic the infant feeding pattern and used to predict their drug exposures. Primaquine and carboxyprimaquine exposures in infants are <1% of the exposure in mothers. Therefore, even in infants with the most severe G6PD deficiency variants, it is highly unlikely that standard doses of primaquine (0.25-1 mg base/kg once daily given to the mother for 1-14 days) would cause significant haemolysis. After the neonatal period, primaquine should not be restricted for breastfeeding women (Clinical Trials Registration: NCT01780753).

Numerical evaluation of internal femur osteosynthesis based on a biomechanical model of the loading in the proximal equine hindlimb.

Femoral fractures are often considered lethal for adult horses because femur osteosynthesis is still a surgical challenge. For equine femur osteosynthesis, primary stability is essential, but the detailed physiological forces occurring in the hindlimb are largely unknown. The objective of this study was to create a numerical testing environment to evaluate equine femur osteosynthesis based on physiological conditions. The study was designed as a finite element analysis (FEA) of the femur using a musculoskeletal model of the loading situation in stance. Relevant forces were determined in the musculoskeletal model via optimization. The treatment of four different fracture types with an intramedullary nail was investigated in FEA with loading conditions derived from the model. The analyzed diaphyseal fracture types were a transverse (TR) fracture, two oblique fractures in different orientations (OB-ML: medial-lateral and OB-AP: anterior-posterior) and a "gap" fracture (GAP) without contact between the fragments. For the native femur, the most relevant areas of increased stress were located distally to the femoral head and proximally to the caudal side of the condyles. For all fracture types, the highest stresses in the implant material were present in the fracture-adjacent screws. Maximum compressive (-348 MPa) and tensile stress (197 MPa) were found for the GAP fracture, but material strength was not exceeded. The mathematical model was able to predict a load distribution in the femur of the standing horse and was used to assess the performance of internal fixation devices via FEA. The analyzed intramedullary nail and screws showed sufficient stability for all fracture types.

Exploring lipid digestion discrepancies between preterm formula and human milk: Insights from in vitro gastrointestinal digestion and the impact of added milk fat.

Lipids play a pivotal role in the nutrition of preterm infants, acting as a primary energy source. Due to their underdeveloped gastrointestinal systems, lipid malabsorption is common, leading to insufficient energy intake and slowed growth. Therefore, it is critical to explore the reasons behind the low lipid absorption rate in formulas for preterm infants. This study utilized a simulated in intro gastrointestinal digestion model to assess the differences in lipid digestion between preterm human milk and various infant formulas. Results showed that the fatty acid release rates for formulas IF3, IF5, and IF7 were 58.90 %, 56.58 %, and 66.71 %, respectively, lower than human milk's 72.31 %. The primary free fatty acids (FFA) and 2-monoacylglycerol (2-MAG) released during digestion were C14:0, C16:0, C18:0, C18:1n-9, and C18:2n-6, in both human milk and formulas. Notably, the higher release of C16:0 in formulas may disrupt fatty acid balance, impacting lipid absorption. Further investigations are necessary to elucidate lipid absorption differences, which will inform the optimization of lipid content in preterm infant formulas.

A secondary model for the effect of pH on the variability in growth fitness of Listeria innocua strains.

Variability in microbial growth is a keystone of modern Quantitative Microbiological Risk Assessment (QMRA). However, there are still significant knowledge gaps on how to model variability, with the most common assumption being that variability is constant. This is implemented by an error term (with constant variance) added on top of the secondary growth model (for the square root of the growth rate). However, this may go against microbial ecology principles, where differences in growth fitness among bacterial strains would be more prominent in the vicinity of the growth limits than at optimal growth conditions. This study coins the term "secondary models for variability", evaluating whether they should be considered in QMRA instead of the constant strain variability hypothesis. For this, 21 strains of Listeria innocua were used as case study, estimating their growth rate by the two-fold dilution method at pH between 5 and 10. Estimates of between-strain variability and experimental uncertainty were obtained for each pH using mixed-effects models, showing the lowest variability at optimal growth conditions, increasing towards the growth limits. Nonetheless, the experimental uncertainty also increased towards the extremes, evidencing the need to analyze both sources of variance independently. A secondary model was thus proposed, relating strain variability and pH conditions. Although the modelling approach certainly has some limitations that would need further experimental validation, it is an important step towards improving the description of variability in QMRA, being the first model of this type in the field.

Threshold dynamics of a reaction-advection-diffusion schistosomiasis epidemic model with seasonality and spatial heterogeneity.

Most water-borne disease models ignore the advection of water flows in order to simplify the mathematical analysis and numerical computation. However, advection can play an important role in determining the disease transmission dynamics. In this paper, we investigate the long-term dynamics of a periodic reaction-advection-diffusion schistosomiasis model and explore the joint impact of advection, seasonality and spatial heterogeneity on the transmission of the disease. We derive the basic reproduction number R 0 and show that the disease-free periodic solution is globally attractive when R 0 < 1 whereas there is a positive endemic periodic solution and the system is uniformly persistent in a special case when R 0 > 1 . Moreover, we find that R 0 is a decreasing function of the advection coefficients which offers insights into why schistosomiasis is more serious in regions with slow water flows.

Modelling phytoplankton-virus interactions: phytoplankton blooms and lytic virus transmission.

A dynamic reaction-diffusion model of four variables is proposed to describe the spread of lytic viruses among phytoplankton in a poorly mixed aquatic environment. The basic ecological reproductive index for phytoplankton invasion and the basic reproduction number for virus transmission are derived to characterize the phytoplankton growth and virus transmission dynamics. The theoretical and numerical results from the model show that the spread of lytic viruses effectively controls phytoplankton blooms. This validates the observations and experimental results of Emiliana huxleyi-lytic virus interactions. The studies also indicate that the lytic virus transmission cannot occur in a low-light or oligotrophic aquatic environment.

Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach.

Subcutaneous injections are an increasingly prevalent route of administration for delivering biological therapies including monoclonal antibodies (mAbs). Compared with intravenous delivery, subcutaneous injections reduce administration costs, shorten the administration time, and are strongly preferred from a patient experience point of view. An understanding of the absorption process of a mAb from the injection site to the systemic circulation is critical to the process of subcutaneous mAb formulation development. In this study, we built a model to predict the absorption rate constant (ka), which denotes how fast a mAb is absorbed from the site of administration. Once trained, our model (enabled by the XGBoost algorithm in machine learning) can predict the ka of a mAb following a subcutaneous injection using in silico molecular properties alone (generated from the primary sequence). Our model does not need clinically observed plasma concentration-time data; this is a novel capability not previously achieved in predictive pharmacokinetic models. The model also showed improved performance when benchmarked against a recently reported mechanistic model that relied on clinical data to predict subcutaneous absorption of mAbs. We further interpreted the model to understand which molecular properties affect the absorption rate and showed that our findings are consistent with previous studies evaluating subcutaneous absorption through direct experimentation. Taken altogether, this study reports the development, validation, benchmarking, and interpretation of a model that can predict the clinical ka of a mAb using its primary sequence as the only input.

The Warburg Effect Reinterpreted 100 yr on: A First-Principles Stoichiometric Analysis and Interpretation from the Perspective of ATP Metabolism in Cancer Cells.

The Warburg Effect is a longstanding enigma in cancer biology. Despite the passage of 100 yr since its discovery, and the accumulation of a vast body of research on the subject, no convincing biochemical explanation has been given for the original observations of aerobic glycolysis in cancer cell metabolism. Here, we have worked out a first-principles quantitative analysis of the problem from the principles of stoichiometry and available electron balance. The results have been interpreted using Nath's unified theory of energy coupling and adenosine triphosphate (ATP) synthesis, and the original data of Warburg and colleagues have been analyzed from this new perspective. Use of the biomass yield based on ATP per unit substrate consumed, [Formula: see text], or the Nath-Warburg number, NaWa has been shown to excellently model the original data on the Warburg Effect with very small standard deviation values, and without employing additional fitted or adjustable parameters. Based on the results of the quantitative analysis, a novel conservative mechanism of synthesis, utilization, and recycling of ATP and other key metabolites (eg, lactate) is proposed. The mechanism offers fresh insights into metabolic symbiosis and coupling within and/or among proliferating cells. The fundamental understanding gained using our approach should help in catalyzing the development of more efficient metabolism-targeting anticancer drugs.

How do seasonal temperature variations influence interplay between toxic and non-toxic cyanobacterial blooms? Evidence from modeling and experimental data.

Summer cyanobacterial blooms exhibit a dynamic interplay between toxic and non-toxic genotypes, significantly influencing the cyanotoxin levels within a lake. The challenge lies in accurately predicting these toxin concentrations due to the significant temporal fluctuations in the proportions of toxic and non-toxic genotypes. Typically, the toxic genotypes dominate during the early and late summer periods, while the non-toxic variants prevail in mid-summer. To dissect this phenomenon, we propose a model that accounts for the competitive interaction between toxic and non-toxic genotypes, as well as seasonal temperature variations. Our numerical simulations suggest that the optimal temperature of the toxic genotypes is lower than that of the optimal temperatures of the non-toxic counterparts. This difference of optimal temperature may potentially contribute to explain the dominance of toxic genotypes at the early and late summer periods, situation often observed in the field. Experimental data from the laboratory align qualitatively with our simulation results, enabling a better understanding of complex interplays between toxic and non-toxic cyanobacteria.

Impact of Obesity on Hepatic Drug Clearance: What are the Influential Variables?

Drug clearance in obese subjects varies widely among different drugs and across subjects with different severity of obesity. This study investigates correlations between plasma clearance (CLp) and drug- and patient-related characteristics in obese subjects, and evaluates the systematic accuracy of common weight-based dosing methods. A physiologically-based pharmacokinetic (PBPK) modeling approach that uses recent information on obesity-related changes in physiology was used to simulate CLp for a normal-weight subject (body mass index [BMI] = 20) and subjects with various severities of obesity (BMI 25-60) for hypothetical hepatically cleared drugs with a wide range of properties. Influential variables for CLp change were investigated. For each drug and obese subject, the exponent that yields perfect allometric scaling of CLp from normal-weight subjects was assessed. Among all variables, BMI and relative changes in enzyme activity resulting from obesity proved highly correlated with obesity-related CLp changes. Drugs bound to α1-acid glycoprotein (AAG) had lower CLp changes compared to drugs bound to human serum albumin (HSA). Lower extraction ratios (ER) corresponded to higher CLp changes compared to higher ER. The allometric exponent for perfect scaling ranged from -3.84 to 3.34 illustrating that none of the scaling methods performed well in all situations. While all three dosing methods are generally systematically accurate for drugs with unchanged or up to 50% increased enzyme activity in subjects with a BMI below 30 kg/m2, in any of the other cases, information on the different drug properties and severity of obesity is required to select an appropriate dosing method for individuals with obesity.

Reverse engineering morphogenesis through Bayesian optimization of physics-based models.

Morphogenetic programs coordinate cell signaling and mechanical interactions to shape organs. In systems and synthetic biology, a key challenge is determining optimal cellular interactions for predicting organ shape, size, and function. Physics-based models defining the subcellular force distribution facilitate this, but it is challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the experimentally observed organ shapes. This integrative framework employs Gaussian Process Regression, a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that maintain the final organ shape. We calibrated and tested the method on Drosophila wing imaginal discs to study mechanisms that regulate epithelial processes ranging from development to cancer. The parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with imaging data of wing discs perturbed with collagenase. The computational pipeline identifies distinct parameter sets mimicking wild-type shapes. It enables a global sensitivity analysis to support the regulation of actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with experimental imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This workflow is extensible toward reverse-engineering morphogenesis across organ systems and for real-time control of complex multicellular systems.

A human pluripotent stem cell-derived in vitro model of the blood-brain barrier in cerebral malaria.

BACKGROUND: Blood-brain barrier (BBB) disruption is a central feature of cerebral malaria (CM), a severe complication of Plasmodium falciparum (Pf) infections. In CM, sequestration of Pf-infected red blood cells (Pf-iRBCs) to brain endothelial cells combined with inflammation, hemolysis, microvasculature obstruction and endothelial dysfunction mediates BBB disruption, resulting in severe neurologic symptoms including coma and seizures, potentially leading to death or long-term sequelae. In vitro models have advanced our knowledge of CM-mediated BBB disruption, but their physiological relevance remains uncertain. Using human induced pluripotent stem cell-derived brain microvascular endothelial cells (hiPSC-BMECs), we aimed to develop a novel in vitro model of the BBB in CM, exhibiting enhanced barrier properties. METHODS: hiPSC-BMECs were co-cultured with HB3var03 strain Pf-iRBCs up to 9 h. Barrier integrity was measured using transendothelial electrical resistance (TEER) and sodium fluorescein permeability assays. Localization and expression of tight junction (TJ) proteins (occludin, zonula occludens-1, claudin-5), cellular adhesion molecules (ICAM-1, VCAM-1), and endothelial surface markers (EPCR) were determined using immunofluorescence imaging (IF) and western blotting (WB). Expression of angiogenic and cell stress markers were measured using multiplex proteome profiler arrays. RESULTS: After 6-h of co-culture with Pf-iRBCs, hiPSC-BMECs showed reduced TEER and increased sodium fluorescein permeability compared to co-culture with uninfected RBCs, indicative of a leaky barrier. We observed disruptions in localization of occludin, zonula occludens-1, and claudin-5 by IF, but no change in protein expression by WB in Pf-iRBC co-cultures. Expression of ICAM-1 and VCAM-1 but not EPCR was elevated in hiPSC-BMECs with Pf-iRBC co-culture compared to uninfected RBC co-culture. In addition, there was an increase in expression of angiogenin, platelet factor-4, and phospho-heat shock protein-27 in the Pf-iRBCs co-culture compared to uninfected RBC co-culture. CONCLUSION: These findings demonstrate the validity of our hiPSC-BMECs based model of the BBB, that displays enhanced barrier integrity and appropriate TJ protein localization. In the hiPSC-BMEC co-culture with Pf-iRBCs, reduced TEER, increased paracellular permeability, changes in TJ protein localization, increase in expression of adhesion molecules, and markers of angiogenesis and cellular stress all point towards a novel model with enhanced barrier properties, suitable for investigating pathogenic mechanisms underlying BBB disruption in CM.

Concentration-QTc modeling of sitravatinib in patients with advanced solid malignancies.

Sitravatinib (MGCD516) is an orally available, small molecule, tyrosine kinase inhibitor that has been evaluated in patients with advanced solid tumors. Concentration-corrected QT interval (QTc; C-QTc) modeling was undertaken, using 767 matched concentration-ECG observations from 187 patients across two clinical studies in patients with advanced solid malignancies, across a dose range of 10-200 mg, via a linear mixed-effects (LME) model. The effect on heart rate (HR)-corrected QT interval via Fridericia's correction method (QTcF) at the steady-state maximum concentration (Cmax,ss) for the sitravatinib proposed therapeutic dosing regimen (100 mg malate once daily [q.d.]) without and with relevant intrinsic and extrinsic factors were predicted. No significant changes in HR from baseline were observed. Hysteresis between sitravatinib plasma concentration and change in QTcF from baseline (ΔQTcF) was not observed. There was no significant relationship between sitravatinib plasma concentration and ΔQTcF. The final C-QTc model predicted a mean (90% confidence interval [CI]) ΔQTcF of 3.92 (1.95-5.89) ms and 2.94 (0.23-6.10) ms at the proposed therapeutic dosing regimen in patients with normal organ function (best case scenario) and patients with hepatic impairment (worst-case scenario), respectively. The upper bounds of the 90% CIs were below the regulatory threshold of concern of 10 ms. The results of the described C-QTc analysis, along with corroborating results from nonclinical safety pharmacology studies, indicate that sitravatinib has a low risk of QTc interval prolongation at the proposed therapeutic dose of 100 mg malate q.d.