Application of machine learning in combination with mechanistic modeling to predict plasma exposure of small molecules

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

The Impact of Stochasticity and Its Control on a Model of the Inflammatory Response

The dysregulation of inflammation, normally a self-limited response that initiates healing, is a critical component of many diseases. Treatment of inflammatory disease is hampered by an incomplete understanding of the complexities underlying the inflammatory response, motivating the application of systems and computational biology techniques in an effort to decipher this complexity and ultimately improve therapy. Many mathematical models of inflammation are based on systems of deterministic equations that do not account for the biological noise inherent at multiple scales, and consequently the effect of such noise in regulating inflammatory responses has not been studied widely. In this work, noise was added to a deterministic system of the inflammatory response in order to account for biological stochasticity. Our results demonstrate that the inflammatory response is highly dependent on the balance between the concentration of the pathogen and the level of biological noise introduced to the inflammatory network. In cases where the pro- and anti-inflammatory arms of the response do not mount the appropriate defense to the inflammatory stimulus, inflammation transitions to a different state compared to cases in which pro- and anti-inflammatory agents are elaborated adequately and in a timely manner. In this regard, our results show that noise can be both beneficial and detrimental for the inflammatory endpoint. By evaluating the parametric sensitivity of noise characteristics, we suggest that efficiency of inflammatory responses can be controlled. Interestingly, the time period on which parametric intervention can be introduced efficiently in the inflammatory system can be also adjusted by controlling noise. These findings represent a novel understanding of inflammatory systems dynamics and the potential role of stochasticity thereon

Daily variation of gene expression in diverse rat tissues

<div><p>Circadian information is maintained in mammalian tissues by a cell-autonomous network of transcriptional feedback loops that have evolved to optimally regulate tissue-specific functions. An analysis of daily gene expression in different tissues, as well as an evaluation of inter-tissue circadian variability, is crucial for a systems-level understanding of this transcriptional circuitry. Affymetrix gene chip measurements of liver, muscle, adipose, and lung tissues were obtained from a rich time series light/dark experiment, involving 54 normal rats sacrificed at 18 time points within the 24-hr cycle. Our analysis revealed a high degree of circadian regulation with a variable distribution of phases among the four tissues. Interestingly, only a small number of common genes maintain circadian activity in all tissues, with many of them consisting of “core-clock” components with synchronous rhythms. Our results suggest that inter-tissue circadian variability is a critical component of homeostatic body function and is mediated by diverse signaling pathways that ultimately lead to highly tissue-specific transcription regulation.</p></div

Temporal profiles of genes maintaining circadian rhythmicity in liver, muscle, adipose, and lung.

<p>Upper panel: Heatmaps showing mean expression data from 3 animals sacrificed at the same time point during three consecutive days. Rows represent the different genes, and columns the mean expression values at the different times of the day. From blue to yellow, the expression intensity is increasing. Ordering of genes in the different rows is based on their phase. Heatmap titles indicate the tissue, and the respective bar plots at the top of each subplot represent the 12 h light (white) 12 h dark (grey) periods. The n is for the number of genes found to retain circadian rhythmicity in each tissue Lower panel: Respective phase histograms for the tissues shown in the upper panel. Circular coordinates indicate the time of day and numbers on the nested circles the number of genes. Dark semicircles at the perimeter of the circles indicate the dark phase.</p

Transcriptional delay and degradation rate parameters estimated to describe the expression of core-clock genes for the different tissues.

<p>Error bars represent the 95% confidence interval. The y-axis provides the parameter values with definitions and units listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197534#pone.0197534.t001" target="_blank">Table 1</a>. All-tissue depicts the parameter values resulting from fitting the data of all tissues concurrently (consensus model).</p

Bar plots showing the different functional groups of the genes for the various combinations of 2-tissues.

<p>Different colors indicate the different functional groups. The x-axis represents the different combination of tissues and the y-axis the percentage of circadian genes that belong to a certain functional group. The n is the number of genes found to maintain circadian oscillations in the respective combinations of tissues.</p

Phase and amplitude variabilities in genes oscillating commonly in all 2-tissue combinations.

<p>Upper panel: Histogram of phase lags of common genes in two tissue combinations. Different colors represent the various tissue combinations. Phase lags are separated into 6 groups on the x-axis, and represent genes that have a phase difference between 0 to 2, 2 to 4, 4 to 6, 6 to 8, 8 to 10, and 10 to 12 hours. The y-axis depicts the percentage of circadian genes retaining a certain phase lag for combinations of certain tissues where Δϕ represent the phase lag (phase difference) in hours. Lower panel: Histogram of % amplitude difference of common genes in two tissue combinations. Different colors represent the various tissue combinations. The % amplitude differences are separated in 5 groups on the x-axis, and represent genes that have a % amplitude difference 0 to 20, 20 to 40, 40 to 60, 60 to 80, and 80 to 100. The y-axis depicts the percentage of circadian genes retaining a certain amplitude difference for combinations of certain tissues where ΔA represent the amplitude difference in %.</p

Conserved genes in mouse and rat for liver, muscle, adipose and lung.

<p>Upper panel: Venn diagrams showing number of genes in each tissue for mouse and rat as well as the number of overlapping genes. Lower panel: Scatterplots of phases (ϕ) in rat and mouse for the different tissues compared with the identity line. Phases for 4 periods were concantenated for visualization purposes. r values at the title of the graphs indicate the circular correlation coefficient.</p