Microfluidic human physiomimetic liver model as a screening platform for drug induced liver injury.

The pre-clinical animal models often fail to predict intrinsic and idiosyncratic drug induced liver injury (DILI), thus contributing to drug failures in clinical trials, black box warnings and withdrawal of marketed drugs. This suggests a critical need for human-relevant in vitro models to predict diverse DILI phenotypes. In this study, a porcine liver extracellular matrix (ECM) based biomaterial ink with high printing fidelity, biocompatibility and tunable rheological and mechanical properties is formulated for supporting both parenchymal and non-parenchymal cells. Further, we applied 3D printing and microfluidic technology to bioengineer a human physiomimetic liver acinus model (HPLAM), recapitulating the radial hepatic cord-like structure with functional sinusoidal microvasculature network, biochemical and biophysical properties of native liver acinus. Intriguingly, the human derived hepatic cells incorporated HPLAM cultured under physiologically relevant microenvironment, acts as metabolic biofactories manifesting enhanced hepatic functionality, secretome levels and biomarkers expression over several weeks. We also report that the matured HPLAM reproduces dose- and time-dependent hepatotoxic response of human clinical relevance to drugs typically recognized for inducing diverse DILI phenotypes as compared to conventional static culture. Overall, the developed HPLAM emulates in vivo like functions and may provide a useful platform for DILI risk assessment to better determine safety and human risk.

The role of anaplerotic metabolism of glucose and glutamine in insulin secretion: A model approach.

We propose a detailed computational beta cell model that emphasizes the role of anaplerotic metabolism under glucose and glucose-glutamine stimulation. This model goes beyond the traditional focus on mitochondrial oxidative phosphorylation and ATP-sensitive K+ channels, highlighting the predominant generation of ATP from phosphoenolpyruvate in the vicinity of KATP channels. It also underlines the modulatory role of H2O2 as a signaling molecule in the first phase of glucose-stimulated insulin secretion. In the second phase, the model emphasizes the critical role of anaplerotic pathways, activated by glucose stimulation via pyruvate carboxylase and by glutamine via glutamate dehydrogenase. It particularly focuses on the production of NADPH and glutamate as key enhancers of insulin secretion. The predictions of the model are consistent with empirical data, highlighting the complex interplay of metabolic pathways and emphasizing the primary role of glucose and the facilitating role of glutamine in insulin secretion. By delineating these crucial metabolic pathways, the model provides valuable insights into potential therapeutic targets for diabetes.

Mechanical models and measurement methods of solid stress in tumors.

In addition to genetic mutations, biomechanical factors also affect the structures and functions of the tumors during tumor growth, including solid stress, interstitial fluid pressure, stiffness, and microarchitecture. Solid stress affects tumors by compressing cancer and stromal cells and deforming blood and lymphatic vessels which reduce supply of oxygen, nutrients and drug delivery, making resistant to treatment. Researchers simulate the stress by creating mechanical models both in vitro and in vivo. Cell models in vitro are divided into two dimensions (2D) and three dimensions (3D). 2D models are simple to operate but exert pressure on apical surface of the cells. 3D models, the multicellular tumor spheres, are more consistent with the actual pathological state in human body. However, the models are more difficult to establish compared with the 2D models. Besides, the procedure of the animal models in vivo is even more complex and tougher to operate. Then, researchers challenged to quantify the solid stress through some measurement methods. We compared the advantages and limitations of these models and methods, which may help to explore new therapeutic targets for normalizing the tumor's physical microenvironment. KEY POINTS: •This is the first review to conclude the mechanical models and measurement methods in tumors. •The merit and demerit of these models and methods are compared. •Insights into further models are discussed.

Evolutionary Game Dynamics with Environmental Feedback in a Network with Two Communities.

Recent developments of eco-evolutionary models have shown that evolving feedbacks between behavioral strategies and the environment of game interactions, leading to changes in the underlying payoff matrix, can impact the underlying population dynamics in various manners. We propose and analyze an eco-evolutionary game dynamics model on a network with two communities such that players interact with other players in the same community and those in the opposite community at different rates. In our model, we consider two-person matrix games with pairwise interactions occurring on individual edges and assume that the environmental state depends on edges rather than on nodes or being globally shared in the population. We analytically determine the equilibria and their stability under a symmetric population structure assumption, and we also numerically study the replicator dynamics of the general model. The model shows rich dynamical behavior, such as multiple transcritical bifurcations, multistability, and anti-synchronous oscillations. Our work offers insights into understanding how the presence of community structure impacts the eco-evolutionary dynamics within and between niches.

Mathematical model for rod outer segment dynamics during retinal detachment.

Retinal detachment (RD) is the separation of the neural layer from the retinal pigmented epithelium thereby preventing the supply of nutrients to the cells within the neural layer of the retina. In vertebrates, primary photoreceptor cells consisting of rods and cones undergo daily renewal of their outer segment through the addition of disc-like structures and shedding of these discs at their distal end. When the retina detaches, the outer segment of these cells begins to degenerate and, if surgical procedures for reattachment are not done promptly, the cells can die and lead to blindness. The precise effect of RD on the renewal process is not well understood. Additionally, a time frame within which reattachment of the retina can restore proper photoreceptor cell function is not known. Focusing on rod cells, we propose a mathematical model to clarify the influence of retinal detachment on the renewal process. Our model simulation and analysis suggest that RD stops or significantly reduces the formation of new discs and that an alternative removal mechanism is needed to explain the observed degeneration during RD. Sensitivity analysis of our model parameters points to the disc removal rate as the key regulator of the critical time within which retinal reattachment can restore proper photoreceptor cell function.

Potential therapeutic targets of gastric cancer explored under endogenous network modeling of clinical data.

Improvement in the survival rate of gastric cancer, a prevalent global malignancy and the leading cause of cancer-related mortality calls for more avenues in molecular therapy. This work aims to comprehend drug resistance and explore multiple-drug combinations for enhanced therapeutic treatment. An endogenous network modeling clinic data with core gastric cancer molecules, functional modules, and pathways is constructed, which is then transformed into dynamics equations for in-silicon studies. Principal component analysis, hierarchical clustering, and K-means clustering are utilized to map the attractor domains of the stochastic model to the normal and pathological phenotypes identified from the clinical data. The analyses demonstrate gastric cancer as a cluster of stable states emerging within the stochastic dynamics and elucidate the cause of resistance to anti-VEGF monotherapy in cancer treatment as the limitation of the single pathway in preventing cancer progression. The feasibility of multiple objectives of therapy targeting specified molecules and/or pathways is explored. This study verifies the rationality of the platform of endogenous network modeling, which contributes to the development of cross-functional multi-target combinations in clinical trials.

Unveiling bacterial communication with a MATLAB GUI implementing the diffusion-based quorum sensing model.

Bacteria employ quorum sensing as a remarkable mechanism for coordinating behaviors and communicating within their communities. In this study, we introduce a MATLAB Graphical User Interface (GUI) that offers a versatile platform for exploring the dynamics of quorum sensing. Our computational framework allows for the assessment of quorum sensing, the investigation of parameter dependencies, and the prediction of minimum biofilm thickness required for its initiation. A pivotal observation from our simulations underscores the pivotal role of the diffusion coefficient in quorum sensing, surpassing the influence of bacterial cell dimensions. Varying the diffusion coefficient reveals significant fluctuations in autoinducer concentration, highlighting its centrality in shaping bacterial communication. Additionally, our GUI facilitates the prediction of the minimum biofilm thickness necessary to trigger quorum sensing, a parameter contingent on the diffusion coefficient. This feature provides valuable insights into spatial constraints governing quorum sensing initiation. The interplay between production rates and cell concentrations emerges as another critical facet of our study. We observe that higher production rates or cell concentrations expedite quorum sensing, underscoring the intricate relationship between cell communication and population dynamics in bacterial communities. While our simulations align with mathematical models reported in the literature, we acknowledge the complexity of living organisms, emphasizing the value of our GUI for standardizing results and facilitating early assessments of quorum sensing. This computational approach offers a window into the environmental conditions conducive to quorum sensing initiation, encompassing parameters such as the diffusion coefficient, cell concentration, and biofilm thickness. In conclusion, our MATLAB GUI serves as a versatile tool for understanding the diverse aspects of quorum sensing especially for non-biologists. The insights gained from this computational framework advance our understanding of bacterial communication, providing researchers with the means to explore diverse ecological contexts where quorum sensing plays a pivotal role.

The Gamma concept approach as a tool to predict fresh produce supporting or not the growth of L. monocytogenes.

Challenge tests are commonly employed to evaluate the growth behavior of L. monocytogenes in food matrices; they are known for being expensive and time-consuming. An alternative could be the use of predictive models to forecast microbial behavior under different conditions. In this study, the growth behavior of L. monocytogenes in different fresh produce was evaluated using a predictive model based on the Gamma concept considering pH, water activity (aw), and temperature as input factors. An extensive literature search resulted in a total of 105 research articles selected to collect growth/no growth behavior data of L. monocytogenes. Up to 808 L. monocytogenes behavior values and physicochemical characteristics were extracted for different fruits and vegetables. The predictive performance of the model as a tool for identifying the produce commodities supporting the growth of L. monocytogenes was proved by comparing with the experimental data collected from the literature. The model provided satisfactory predictions on the behavior of L. monocytogenes in vegetables (>80% agreement with experimental observations). For leafy greens, a 90% agreement was achieved. In contrast, the performance of the Gamma model was less satisfactory for fruits, as it tends to overestimate the potential of acid commodities to inhibit the growth of L. monocytogenes.

A Computational Framework for the Administration of 5-Aminovulinic Acid Before Glioblastoma Surgery.

5-Aminolevulinic Acid (5-ALA) is the only fluorophore approved by the FDA as an intraoperative optical imaging agent for fluorescence-guided surgery in patients with glioblastoma. The dosing regimen is based on rodent tests where a maximum signal occurs around 6 h after drug administration. Here, we construct a computational framework to simulate the transport of 5-ALA through the stomach, blood, and brain, and the subsequent conversion to the fluorescent agent protoporphyrin IX at the tumor site. The framework combines compartmental models with spatially-resolved partial differential equations, enabling one to address questions regarding quantity and timing of 5-ALA administration before surgery. Numerical tests in two spatial dimensions indicate that, for tumors exceeding the detection threshold, the time to peak fluorescent concentration is 2-7 h, broadly consistent with the current surgical guidelines. Moreover, the framework enables one to examine the specific effects of tumor size and location on the required dose and timing of 5-ALA administration before glioblastoma surgery.

Adapting to the Abyss: Passive Ventilation in the Deep-Sea Glass Sponge Euplectella aspergillum.

We analyze the flow physics inside the body cavity and downstream the deep-sea glass sponge Euplectella aspergillum. We provide evidence that the helical skeletal motifs of the sponge give rise to a rich fluid dynamic field, allowing the organism to scavenge flow from the bottom of the sea and promoting a spontaneous, organized vertical flow within its body cavity toward the osculum. Our analysis points at a functional adaptation of the organism, which can passively divert flow through the osculum in unfavorable, low ambient currents, with no need for active pumping, with potential repercussions in functional ecology, as well as the design of chemical reactors, air-treatment units, and civil and aeronaval structures.

Multiflagellate Swimming Controlled by Hydrodynamic Interactions.

Many eukaryotic microorganisms propelled by multiple flagella can swim very rapidly with distinct gaits. Here, we model a three-dimensional mutiflagellate swimmer, resembling the microalgae. When the flagella are actuated synchronously, the swimming efficiency can be enhanced or reduced by interflagella hydrodynamic interactions (HIs), determined by the intrinsic tilting angle of the flagella. The asynchronous gait with a phase difference between neighboring flagella can reduce oscillatory motion via the basal mechanical coupling. In the presence of a spherical body, simulations taking into account the flagella-body interactions reveal the advantage of anterior configuration compared with posterior configuration, where in the latter case an optimal flagella number arises. Apart from understanding the role of HIs in the multiflagellate microorganisms, this work could also guide laboratory fabrications of novel microswimmers.

Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling.

Engineering natural microbiomes for biotechnological applications remains challenging, as metabolic interactions within microbiomes are largely unknown, and practical principles and tools for microbiome engineering are still lacking. Here, we present a combinatory top-down and bottom-up framework to engineer natural microbiomes for the construction of function-enhanced synthetic microbiomes. We show that application of herbicide and herbicide-degrader inoculation drives a convergent succession of different natural microbiomes toward functional microbiomes (e.g., enhanced bioremediation of herbicide-contaminated soils). We develop a metabolic modeling pipeline, SuperCC, that can be used to document metabolic interactions within microbiomes and to simulate the performances of different microbiomes. Using SuperCC, we construct bioremediation-enhanced synthetic microbiomes based on 18 keystone species identified from natural microbiomes. Our results highlight the importance of metabolic interactions in shaping microbiome functions and provide practical guidance for engineering natural microbiomes.

Cell microencapsulation techniques for cancer modelling and drug discovery.

Cell encapsulation into spherical microparticles is a promising bioengineering tool in many fields, including 3D cancer modelling and pre-clinical drug discovery. Cancer microencapsulation models can more accurately reflect the complex solid tumour microenvironment than 2D cell culture and therefore would improve drug discovery efforts. However, these microcapsules, typically in the range of 1 - 5000 µm in diameter, must be carefully designed and amenable to high-throughput production. This review therefore aims to outline important considerations in the design of cancer cell microencapsulation models for drug discovery applications and examine current techniques to produce these. Extrusion (dripping) droplet generation and emulsion-based techniques are highlighted and their suitability to high-throughput drug screening in terms of tumour physiology and ease of scale up is evaluated.

3D microencapsulation models of cancer offer a customisable platform to mimic key aspects of solid tumour physiology in vitro. However, many 3D models do not recapitulate the hypoxic conditions and altered tissue stiffness established in many tumour types and stages. Furthermore, microparticles for cancer cell encapsulation are commonly produced using methods that are not necessarily suitable for scale up to high-throughput manufacturing. This review aims to evaluate current technologies for cancer cell-laden microparticle production with a focus on physiological relevance and scalability. Emerging techniques will then be touched on, for production of uniform microparticles suitable for high-throughput drug discovery applications.

Taxis-driven complex patterns of a plankton model.

This paper reports an important conclusion that self-diffusion is not a necessary condition for inducing Turing patterns, while taxis could establish complex pattern phenomena. We investigate pattern formation in a zooplankton-phytoplankton model incorporating phytoplankton-taxis, where phytoplankton-taxis describes the zooplankton that tends to move toward the high-densities region of the phytoplankton population. By using the phytoplankton-taxis sensitivity coefficient as the Turing instability threshold, one shows that the model exhibits Turing instability only when repulsive phytoplankton-taxis is added into the system, while the attractive-type phytoplankton-taxis cannot induce Turing instability of the system. In addition, the system does not exhibit Turing instability when the phytoplankton-taxis disappears. Numerically, we display the complex patterns in 1D, 2D domains and on spherical and zebra surfaces, respectively. In summary, our results indicate that the phytoplankton-taxis plays a pivotal role in giving rise to the Turing pattern formation of the model. Additionally, these theoretical and numerical results contribute to our understanding of the complex interaction dynamics between zooplankton and phytoplankton populations.

Chaos and bifurcations of a two-dimensional hepatitis C virus model with hepatocyte homeostasis.

In this paper, we delve into the intricate local dynamics at equilibria within a two-dimensional model of hepatitis C virus (HCV) alongside hepatocyte homeostasis. The study investigates the existence of bifurcation sets and conducts a comprehensive bifurcation analysis to elucidate the system's behavior under varying conditions. A significant focus lies on understanding how changes in parameters can lead to bifurcations, which are pivotal points where the qualitative behavior of the system undergoes fundamental transformations. Moreover, the paper introduces and employs hybrid control feedback and Ott-Grebogi-Yorke strategies as tools to manage and mitigate chaos inherent within the HCV model. This chaos arises due to the presence of flip and Neimark-Sacker bifurcations, which can induce erratic behavior in the system. Through the implementation of these control strategies, the study aims to stabilize the system and restore it to a more manageable and predictable state. Furthermore, to validate the theoretical findings and the efficacy of the proposed control strategies, extensive numerical simulations are conducted. These simulations serve as a means of confirming the theoretical predictions and provide insight into the practical implications of the proposed control methodologies. By combining theoretical analysis with computational simulations, the paper offers a comprehensive understanding of the dynamics of the HCV model and provides valuable insights into potential strategies for controlling and managing chaos in such complex biological systems.

Population Pharmacokinetic Models of Venetoclax in Hematologic Malignancies: A Systematic Review.

Several population pharmacokinetic (PPK) models of B cell lymphoma-2 (BCL-2) venetoclax (VEN) have been developed and published to characterize the influencing factors of pharmacokinetics in hematologic malignancies. This review described PPK models of VEN examining the magnitude and types of covariate effects in PK parameters, as well as identified areas that require further investigation in order to facilitate their use. Currently, there are six analyses on PPK models of VEN summarized in this review. Most analyses described the pharmacokinetics of VEN with a two-compartment model and all covariates are categorical. The median estimated apparent clearance (CL/F) was 446 L/Day and apparent volume of distribution of the central compartment (V2/F) was 114.5 L. The median IIV of CL/F reported was 39.5% and V2/F was 46.7%. Most commonly, CYP3A inhibitors, OATP1B3 inhibitors and rituximab co-administration were found to be significant covariates on CL/F. In addition, sex and population were influential covariates on V2/F. A detailed description of the characteristics of PPK models of VEN is provided in this review, as well as the effects of covariates on the PK parameters. For future development of the VEN PPK model, CYP3A inhibitors, rituximab co-administration, OATP1B1 transporter inhibitors, sex, population, and food might be considered. Further research and comprehensive investigations should be undertaken to explore reference ranges for therapeutic drug monitoring, define the potential role of patients with cerebrospinal fluid complications, and assess new or potential covariates. These endeavors will facilitate the development of personalized VEN therapy.

Establishment of a Four-Cell In Vitro Blood-Brain Barrier Model With Human Primary Brain Cells.

The blood-brain barrier (BBB) constitutes a crucial protective anatomical layer with a microenvironment that tightly controls material transit. Constructing an in vitro BBB model to replicate in vivo features requires the sequential layering of constituent cell types. Maintaining heightened integrity in the observed tight junctions during both the establishment and post-experiment phases is crucial to the success of these models. We have developed an in vitro BBB model that replicates the cellular composition and spatial orientation of in vivo BBB observed in humans. The experiment includes comprehensive procedures and steps aimed at enhancing the integration of the four-cell model. Departing from conventional in vitro BBB models, our methodology eliminates the necessity for pre-coated plates to facilitate cell adhesion, thereby improving cell visualization throughout the procedure. An in-house coating strategy and a simple yet effective approach significantly reduce costs and provides superior imaging of cells and corresponding tight junction protein expression. Also, our BBB model includes all four primary cell types that are structural parts of the human BBB. With its innovative and user-friendly features, our in-house optimized in vitro four-cell-based BBB model showcases novel methodology and provides a promising experimental platform for drug screening processes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Coating and culture system Basic Protocol 2: Cell seeding and Transwell insert handling Basic Protocol 3: Assessment of model functionality.

Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events.

Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, a data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK model`s performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors.

Cooperative growth in microbial communities is a driver of multistability.

Microbial communities often exhibit more than one possible stable composition for the same set of external conditions. In the human microbiome, these persistent changes in species composition and abundance are associated with health and disease states, but the drivers of these alternative stable states remain unclear. Here we experimentally demonstrate that a cross-kingdom community, composed of six species relevant to the respiratory tract, displays four alternative stable states each dominated by a different species. In pairwise coculture, we observe widespread bistability among species pairs, providing a natural origin for the multistability of the full community. In contrast with the common association between bistability and antagonism, experiments reveal many positive interactions within and between community members. We find that multiple species display cooperative growth, and modeling predicts that this could drive the observed multistability within the community as well as non-canonical pairwise outcomes. A biochemical screening reveals that glutamate either reduces or eliminates cooperativity in the growth of several species, and we confirm that such supplementation reduces the extent of bistability across pairs and reduces multistability in the full community. Our findings provide a mechanistic explanation of how cooperative growth rather than competitive interactions can underlie multistability in microbial communities.

Integrated PBPK-EO modeling of osimertinib to predict plasma concentrations and intracranial EGFR engagement in patients with brain metastases.

The purpose of this study was to develop and validate a physiologically based pharmacokinetic (PBPK) model combined with an EGFR occupancy (EO) model for osimertinib (OSI) to predict plasma trough concentration (Ctrough) and the intracranial time-course of EGFR (T790M and L858R mutants) engagement in patient populations. The PBPK model was also used to investigate the key factors affecting OSI pharmacokinetics (PK) and intracranial EGFR engagement, analyze resistance to the target mutation C797S, and determine optimal dosing regimens when used alone and in drug-drug interactions (DDIs). A population PBPK-EO model of OSI was developed using physicochemical, biochemical, binding kinetic, and physiological properties, and then validated using nine clinical PK studies, observed EO study, and two clinical DDI studies. The PBPK-EO model demonstrated good consistency with observed data, with most prediction-to-observation ratios falling within the range of 0.7 to 1.3 for plasma AUC, Cmax, Ctrough and intracranial free concentration. The simulated time-course of C797S occupancy by the PBPK model was much lower than T790M and L858R occupancy, providing an explanation for OSI on-target resistance to the C797S mutation. The PBPK model identified ABCB1 CLint,u, albumin level, and EGFR expression as key factors affecting plasma Ctrough and intracranial EO for OSI. Additionally, PBPK-EO simulations indicated that the optimal dosing regimen for OSI in patients with brain metastases is either 80 mg once daily (OD) or 160 mg OD, or 40 mg or 80 mg twice daily (BID). When used concomitantly with CYP enzyme perpetrators, the PBPK-EO model suggested appropriate dosing regimens of 80 mg OD with fluvoxamine (FLUV) itraconazole (ITR) or fluvoxamine (FLUC) for co-administration and an increase to 160 mg OD with rifampicin (RIF) or efavirenz (EFA). In conclusion, the PBPK-EO model has been shown to be capable of simulating the pharmacokinetic concentration-time profiles and the time-course of EGFR engagement for OSI, as well as determining the optimum dosing in various clinical situations.