Frontiers in systems biology最新文献

Prediction of a new molecule's exposure in plasma is a critical first step toward understanding its efficacy/toxicity profile and concluding whether it is a possible first-in-class, best-in-class candidate. For this prediction, traditional pharmacometrics use a variety of scaling methods that are heavily based on pre-clinical pharmacokinetic (PK) data. We here propose a novel framework based on which preclinical exposure prediction is performed by applying machine learning (ML) in tandem with mechanism-based modeling. In our proposed method, a relationship is initially established between molecular structure and physicochemical (PC)/PK properties using ML, and then the ML-driven PC/PK parameters are used as input to mechanistic models that ultimately predict the plasma exposure of new candidates. To understand the feasibility of our proposed framework, we evaluated a number of mechanistic models (1-compartment, physiologically based pharmacokinetic (PBPK)), PBPK distribution models (Berezhkovskiy, PK-Sim standard, Poulin and Theil, Rodgers and Rowland, and Schmidt), and PBPK parameterizations (using in vivo, or in vitro clearance). For most of the scenarios tested, our results demonstrate that PK profiles can be adequately predicted based on the proposed framework. Our analysis further indicates some limitations when liver microsomal intrinsic clearance (CLint) is used as the only clearance pathway and underscores the necessity of investigating the variability emanating from the different distribution models when providing PK predictions. The suggested approach aims at earlier exposure prediction in the drug development process so that critical decisions on molecule screening, chemistry design, or dose selection can be made as early as possible.

Mathematical modeling has made significant contributions to drug design, development, and optimization. Virtual clinical trials that integrate mathematical models to explore patient heterogeneity and its impact on a variety of therapeutic questions have recently risen in popularity. Here, we outline best practices for creating virtual patients from mathematical models to ultimately implement and execute a virtual clinical trial. In this practical guide, we discuss and provide examples of model design, parameter estimation, parameter sensitivity, model identifiability, and virtual patient cohort creation. Our goal is to help researchers adopt these approaches to further the use of virtual population-based analysis and virtual clinical trials.

Alzheimer's disease (AD), a neurodegenerative disorder, is characterized by its ability to cause memory loss and damage other cognitive functions. Aggregation of amyloid beta (Aβ) plaques and neurofibrillary tangles in the brain are responsible for the development of Alzheimer's disease (AD). While attempts targeting Aβ and tau proteins have been extensively conducted in the past decades, only two FDA-approved drugs (i.e., monoclonal antibodies) tackle the underlying biology of Alzheimer's disease. In this study, an integrated computational framework was developed to identify new drug targets for Alzheimer's disease and identify small molecules as potential therapeutical options. A systematic investigation of the gene networks firstly revealed that the Apolipoprotein E4 (ApoE4) gene plays a central role among genes associated with Alzheimer's disease. The ApoE4 protein was then chosen as the protein target based on its role in the main pathological hallmarks of AD, which has been shown to increase Aβ accumulation by directly binding to Aβ as well as interfering with Aβ clearance that is associated with other receptors. A library of roughly 1.5 million compounds was then virtually screened via a ligand-protein docking program to identify small-molecule compounds with potential binding capacity to the ApoE4 N-terminal domain. On the basis of compound properties, 312 compounds were selected, analyzed and clustered to further identify common structures and essential functional groups that play an important role in binding ApoE4. The in silico prediction suggested that compounds with four common structures of sulfon-amine-benzene, 1,2-benzisothiazol-3-amine 1,1-dioxide, N-phenylbenzamide, and furan-amino-benzene presented strong hydrogen bonds with residues E27, W34, R38, D53, D153, or Q156 in the N terminal of ApoE4. These structures might also form strong hydrophobic interactions with residues W26, E27, L28, L30, G31, L149, and A152. While the 312 compounds can serve as drug candidates for further experiment assays, the four common structures, along with the residues for hydrogen bond or hydrophobic interaction, pave the foundation to further optimize the compounds as better binders of ApoE4.

The recent surge in single-cell genomics, including the development of a wide range of experimental and computational approaches, has provided insights into the complex molecular networks of cells during development and in human diseases at unprecedented resolution. Single-cell transcriptome analysis has enabled high-resolution investigation of cellular heterogeneity in a wide range of cell populations ranging from early embryos to complex tissues-while posing the risk of only capturing a partial picture of the cells' complex molecular networks. Single-cell multiomics technologies aim to bridge this gap by providing a more holistic view of the cell by simultaneously measuring multiple molecular types from the same cell and providing a more complete view of the interactions and combined functions of multiple regulatory layers at cell-type resolution. In this review, we briefly summarized the recent advances in multimodal single-cell technologies and discussed the challenges and opportunities of the field.

Elucidating cell regulation remains a challenging task due to the complexity of metabolism and the difficulty of experimental measurements. Here we present a method for prediction of cell regulation to maximize cell growth rate while maintaining the solvent capacity of the cell. Prediction is formulated as an optimization problem using a thermodynamic framework that can leverage experimental data. We develop a formulation and variable initialization procedure that allows for computing solutions of the optimization with an interior point method. The approach is applied to photoheterotrophic growth of Rhodospirilium rubrum using ethanol as a carbon source, which has applications to biosynthesis of ethylene production. Growth is captured as the rate of synthesis of amino acids into proteins, and synthesis of nucleotide triphoshaptes into RNA and DNA. The method predicts regulation that produces a high rate of protein and RNA synthesis while DNA synthesis is reduced close to zero in agreement with production of DNA being turned off for much of the cell cycle.

The metabolic axis linking the gut microbiome and heart is increasingly being researched in the context of cardiovascular health. The gut microbiota-derived trimethylamine/trimethylamine N-oxide (TMA/TMAO) pathway is responsible along this axis for the bioconversion of dietary precursors into TMA/TMAO and has been implicated in the progression of heart failure and dysbiosis through a positive-feedback interaction. Systems biology approaches in the context of researching this interaction offer an additional dimension for deepening the understanding of metabolism along the gut-heart axis. For instance, genome-scale metabolic models allow to study the functional role of pathways of interest in the context of an entire cellular or even whole-body metabolic network. In this mini review, we provide an overview of the latest findings on the TMA/TMAO super pathway and summarize the current state of knowledge in a curated pathway map on the community platform WikiPathways. The pathway map can serve both as a starting point for continual curation by the community as well as a resource for systems biology modeling studies. This has many applications, including addressing remaining gaps in our understanding of the gut-heart axis. We discuss how the curated pathway can inform a further curation and implementation of the pathway in existing whole-body metabolic models, which will allow researchers to computationally simulate this pathway to further understand its role in cardiovascular metabolism.

Epithelial to mesenchymal transition (EMT) is a complex, non-linear, dynamic multistep process that plays an integral role in the development of metastatic cancers. A diverse range of signaling molecules, along with their associated pathways, were observed to be involved in promoting EMT and cancer metastasis. Transforming growth factor-β (TGFβ), through its SMAD-dependent and SMAD-independent signaling, orchestrates numerous regulators that converge on key EMT transcription factors (TFs). These TFs further govern the phenotypic transition of cancer cells from epithelial to mesenchymal states. This study explores the TGFβ signaling pathway and its unique network architecture to understand their information processing roles in EMT. Two coherent type 1 feed forward network motifs regulating the expression of SNAIL and N-cadherin were observed. SNAIL, which is one of the crucial regulators of EMT, links both the coherent type 1 feed forward loops (C1FFLs) leading to hypermotif-like structure (Adler and Medzhitov, 2022). Systems modeling and analysis of these motifs and hypermotifs illustrated several interesting emergent information processing roles of the regulators involved. The known roles of these regulators, as described in the literature, were highly correlated with the emergent properties observed. The motifs illustrated persistence detection and noise filtration in regulating the expression of SNAIL and N-cadherin. Along with these system-level properties, the hypermotif architecture also exhibited temporal expression of GLI, SNAIL, ZEB, and N-cadherin. Furthermore, a hypothetical three-layered C1FFL hypermotif was postulated and analyzed. The analysis revealed various interesting system-level properties. However, possible existence of such real biological networks needs further exploration both theoretically and experimentally. Deciphering these network motifs and hypermotifs has provided an additional understanding of the complex biological phenomenon, such as EMT in cancer metastasis.

Introduction: As people living with HIV age, the increasing burden of multimorbidity poses a significant health challenge. The aims of this study were to identify common patterns of multimorbidity and examine changes in their burden, as well as their associations with risk factors, over a 3-5 year period in people with HIV, enrolled in the Pharmacokinetic and clinical Observations in PeoPle over fiftY (POPPY) study. Methods: Common multimorbidity patterns were identified in POPPY participants with HIV using principal component analysis, based on Somers' D statistic. Multimorbidity burden scores were calculated for each participant/pattern at study entry/follow-up and were standardised relative to the mean in the sample at baseline (scores >0 thus reflect a greater number of comorbidities relative to the mean). Two multivariable linear regression models were fitted to examine the associations between risk factors and burden z-scores at baseline and change in z-scores over a 3-5 year period. Results: Five patterns were identified among the 1073 POPPY participants with HIV {median age [interquartile range (IQR)], 52 (47-59) years; 85% male and 84% white}: Cardiovascular diseases (CVDs), Sexually transmitted diseases (STDs), Neurometabolic, Cancer and Mental-gastro-joint. The multivariable linear regression showed that older age, behavioural factors (i.e., body mass index (BMI), history of injection drug use, current recreational drug use and sex between men), and HIV-specific factors (i.e., duration since HIV diagnosis and a prior AIDS diagnosis) were associated with higher multimorbidity burden at baseline. However, only three of the factors (age, BMI and duration since HIV diagnosis) were significantly associated with an increase in burden across specific patterns over time. Discussion: Key modifiable and non-modifiable factors contributing to an increase in burden of multimorbidity were identified. Our findings may inform the development of more targeted interventions and guidelines to effectively prevent and manage the rising burden of multimorbidity in people with HIV.

Drug combinations have been proposed to combat drug resistance in cancer, but due to the large number of possible drug targets, in vitro testing of all possible combinations of drugs is challenging. Computational models of a disease hold great promise as tools for prediction of response to treatment, and here we constructed a logical model integrating signaling pathways frequently dysregulated in cancer, as well as pathways activated upon DNA damage, to study the effect of clinically relevant drug combinations. By fitting the model to a dataset of pairwise combinations of drugs targeting MEK, PI3K, and TAK1, as well as several clinically approved agents (palbociclib, olaparib, oxaliplatin, and 5FU), we were able to perform model simulations that allowed us to predict more complex drug combinations, encompassing sets of three and four drugs, with potentially stronger effects compared to pairwise drug combinations. All predicted third-order synergies, as well as a subset of non-synergies, were successfully confirmed by in vitro experiments in the colorectal cancer cell line HCT-116, highlighting the strength of using computational strategies to rationalize drug testing.