Biology subjects

Sherfey, J.

Publications and source records attributed to Sherfey, J..

5 recordsLinked to original sources

bioRxiv2025

Predicting Human Pharmacokinetic Parameters of Drugs using a Multi-Tissue Chip Platform Integrating Liver, Kidney, and Skeletal Muscle Microphysiological Systems

Accurate prediction of human pharmacokinetic (PK) parameters remains a critical challenge in drug discovery. While simple in vitro assays provide insight into discrete metabolic processes, they cannot recapitulate the complex multi-organ interactions that involve absorption, distribution, metabolism, and excretion (ADME) processes. This limitation has perpetuated a heavy reliance on animal experimentation to predict integrated outcomes like hepatic and renal clearance in humans. In this study, we developed a multi-tissue chip (MTC) to evaluate the PK parameters of intravenously (IV) administered drugs. The MTC platform provides continuous oxygenation through media recirculation and interconnects three MPSs (liver, kidney, skeletal muscle) to study drug metabolism, excretion and distribution within a single in vitro system. The functionality of each MPS was characterized for more than 15 days with single- and multi-tissue chips. A diverse set of drugs from all extended clearance classification system (ECCS) classes with various clearance mechanisms was evaluated on the MTCs and on-chip PK parameters were evaluated, such as hepatic metabolism, uptake, and disposition, tubular secretion and reabsorption, and muscle distribution. These PK parameters were successfully correlated to clinical parameters for IV drugs: hepatic clearance (CLH), renal clearance (CLR), volume of distribution (VD) and then these parameters were extrapolated to human drug exposures using physiologically-based pharmacokinetic (PBPK) modeling. This study demonstrated that the MTC platform could deliver accurate prediction for clinical pharmacokinetics of intravenously administered small molecules without the need for animal studies.

bioengineering↗

bioRxiv2024

Predictive routing emerges from self-supervised stochastic neural plasticity

Predictive processing theories propose that the brain builds internal models of its environment by reducing the discrepancy between internally generated predictions and external sensory signals. Prior work has linked these processes to oscillatory activity in gamma (40-100 Hz) and alpha/beta (10-30 Hz) frequency ranges. Current computational approaches face a trade-off: abstract predictive-processing models can implement self-supervised computations but often omit oscillatory spiking dynamics, whereas biophysically constrained spiking models can generate neural rhythms but often require extensive manual tuning. Here, we introduce the Genetic Stochastic Delta Rule (GSDR), an evolutionary optimization framework for fitting nonlinear neural models to electrophysiological objectives. We first evaluate GSDR in simplified optimization settings, then apply it to spiking-network objectives involving firing rates, beta/gamma spectral ratios, and empirical macaque stimulus-evoked gamma dynamics from visual cortex. We show that GSDR can search constrained synaptic parameter spaces, reduce reliance on manual tuning, and reproduce spectral and circuit-level phenotypes associated with predictive routing. We also used Izhikevich simulations as a model-class robustness analysis, showing that the approach is not limited to the original Hodgkin-Huxley-style implementation. These results position GSDR as a methodological framework for multi-objective exploration of oscillatory neural models. Author summaryIn predictive processing theories, the brain is hypothesized to build internal models of its environment. Empirical and theoretical studies suggest that neuronal oscillations are important components of this process, and abnormal oscillations are also linked to disorders such as schizophrenia. To study such mechanisms, computational neuroscience needs models that can express biologically meaningful spiking and oscillatory dynamics while also being trainable without extensive manual tuning. We developed the Genetic Stochastic Delta Rule (GSDR), a self-supervised evolutionary optimization framework for fitting nonlinear neural models to objectives. GSDR combines objective-guided search, stochastic exploration, genetic selection/deselection, and an activity-dependent MCDP update term. We show that GSDR can tune spiking networks toward beta/gamma spectral objectives and empirical stimulus-evoked gamma dynamics. The results do not prove predictive routing or identify a unique biological circuit; rather, they show that GSDR can identify candidate circuit configurations and can generalize beyond the original Hodgkin-Huxley-style model to Izhikevich simulations.

neuroscience↗

bioRxiv2024

Pathway-based similarity measurement to quantify transcriptomics similarity between human tissues and preclinical models.

Accurate clinical translation of preclinical research remains challenging, primarily due to species-specific differences and disease and patient heterogeneity. An important recent advancement has been development of microphysiological systems that consist of multiple human cell types that recapitulate key characteristics of their respective human systems, allowing essential physiologic processes to be accurately assessed during drug development. However, an unmet need remains regarding a quantitative method to evaluate the similarity between diverse sample types for various contexts of use (CoU)-specific pathways. To address this gap, this study describes the development of pathway-based similarity measurement (PBSM), which leverages RNA-seq data and pathway-based information to assess the human relevance of preclinical models for specific CoU. PBSM offers a quantitative method to compare the transcriptomic similarity of preclinical models to human tissues, shown here as proof of concept for liver and cardiac tissues, enabling improved model selection and validation. Thus, PBSM can successfully support CoU selection for preclinical models, assess the impact of different gene sets on similarity calculations, and differentiate among various in vitro and in vivo models. PBSM has potential to reduce the translational gap in drug development by allowing quantitative evaluation of the similarity of preclinical models to human tissues, facilitating model selection, and improving understanding of context-specific applications. PBSM can serve as a foundation for enhancing the physiological relevance of in vitro models and supporting the development of more effective therapeutic interventions.

bioinformatics↗

bioRxiv2023

A Novel Milli-fluidic Liver Tissue Chip with continuous Recirculation for Predictive pharmacokinetics Applications

A crucial step in lead selection during drug development is accurate estimation and optimization of hepatic clearance using in vitro methods. However, current methods are limited by factors such as lack of physiological relevance, short culture/incubation times that are not consistent with drug exposure patterns in patients, use of drug absorbing materials, and evaporation during long-term incubation. To address these technological needs, we developed a novel milli-fluidic human liver tissue chip (LTC) that was designed with continuous media recirculation and optimized for hepatic cultures using human primary hepatocytes. Here, we characterized the LTC using a series of physiologically relevant metrics and test compounds to demonstrate that we could accurately predict the PK of both low and high clearance compounds. The non-biological characterization indicated that the cyclic olefin copolymer (COC)-based LTC exhibited negligible evaporation and minimal non-specific binding of drugs of varying ionic states and lipophilicity. Biologically, the LTC exhibited functional and polarized hepatic culture with sustained metabolic CYP activity for at least 15 days. This long-term culture was then used for drug clearance studies for low and high-clearance compounds for at least 12 days and clearance was estimated for a range of compounds with high in vitro in vivo correlation (IVIVC). We also demonstrated that LTC can be induced by rifampicin, and the culture age had insignificant effect on depletion kinetic and predicted clearance value. Thus, we used advances in bioengineering to develop a novel purpose-built platform with high reproducibility and minimal variability to address unmet needs for PK applications.

pharmacology and toxicology↗

bioRxiv2019

Constraints on Persistent Activity in a Biologically Detailed Network Model of the Prefrontal Cortex with Heterogeneities

Persistent activity, the maintenance of neural activation over short periods of time in cortical networks, is widely thought to underlie the cognitive function of working memory. A large body of modeling studies has reproduced this kind of activity using cell assemblies with strengthened synaptic connections. However, almost all of these studies have considered persistent activity within networks with homogeneous neurons and synapses, making it difficult to judge the validity of such model results for cortical dynamics, which is based on highly heterogeneous neurons. Here, we consider persistent activity in a detailed, strongly data-driven network model of the prefrontal cortex with heterogeneous neuron and synapse parameters. Surprisingly, persistent activity could not be reproduced in this model without incorporating further constraints. We identified three factors that prevent successful persistent activity: heterogeneity in the cell parameters of interneurons, heterogeneity in the parameters of short-term synaptic plasticity and heterogeneity in the synaptic weights. Our model predicts that persistent activity is recovered if the heterogeneity in the activity of individual interneurons is diminished, which could be achieved by a homeostatic plasticity mechanism. Such a plasticity scheme could also compensate the heterogeneities in the synaptic weights and short-term plasticity when applied to the inhibitory synapses. Cell assemblies shaped in this way may be potentially targeted by distinct inputs or become more responsive to specific tuning or spectral properties. Furthermore, the model predicts that a network that exhibits persistent activity is not able to dynamically produce intrinsic in vivo-like irregular activity at the same time, because heterogeneous synaptic connections are required for these dynamics. Thus, the background noise in such a network must either be produced by external input or constitutes an entirely different state of the network, which is brought about, e.g., by neuromodulation.\n\nAuthor summaryTo operate effectively in a constantly changing world, it is crucial to keep relevant information in mind for short periods of time. This ability, called working memory, is commonly assumed to rest on reverberating activity among members of cell assemblies. While effective in reproducing key results of working memory, most cell assembly models rest on major simplifications such as using the same parameters for all neurons and synapses, i.e., assuming homogeneity in these parameters. Here, we show that this homogeneity assumption is necessary for persistent activity to arise, specifically for inhibitory interneurons and synapses. Using a strongly data-driven network model of the prefrontal cortex, we show that the heterogeneities in the above parameters that are implied by in vitro studies prevent persistent activity. When homogeneity is imposed on inhibitory neurons and synapses, persistent activity is recovered. We propose that the homogeneity constraints can be implemented in the brain by means of homeostatic plasticity, a form of learning that keeps the activity of a network in a constant, homeostatic state. The model makes a number of predictions for biological networks, including a structural separation of networks responsible for generating persistent activity and spontaneous, noise-like activity.

neuroscience↗