Development of a correlation based and a decision tree based prediction algorithm for tissue to plasma partition coefficients

Physiologically based pharmacokinetic (PBPK) modeling is a tool used in drug discovery and human health risk assessment. PBPK models are mathematical representations of the anatomy, physiology and biochemistry of an organism. PBPK models, using both compound and physiologic inputs, are used to predict a drug’s pharmacokinetics in various situations. Tissue to plasma partition coefficients (Kp), a key PBPK model input, define the steady state concentration differential between the tissue and plasma and are used to predict the volume of distribution. Experimental determination of these parameters once limited the development of PBPK models however in silico prediction methods were introduced to overcome this issue. The developed algorithms vary in input parameters and prediction accuracy and none are considered standard, warranting further research. Chapter 2 presents a newly developed Kp prediction algorithm that requires only readily available input parameters. Using a test dataset, this Kp prediction algorithm demonstrated good prediction accuracy and greater prediction accuracy than preexisting algorithms. Chapter 3 introduced a decision tree based Kp prediction method. In this novel approach, six previously published algorithms, including the one developed in Chapter 2, were utilized. The aim of the developed classifier was to identify the most accurate tissue-specific Kp prediction algorithm for a new drug. A dataset consisting of 122 drugs was used to train the classifier and identify the most accurate Kp prediction algorithm for a certain physico-chemical space. Three versions of tissue specific classifiers were developed and were dependent on the necessary inputs. The use of the classifier resulted in a better prediction accuracy as compared to the use of any single Kp prediction algorithm for all tissues; the current mode of use in PBPK model building. With built-in estimation equations for those input parameters not necessarily available, this Kp prediction tool will provide Kp prediction when only limited input parameters are available. The two presented innovative methods will improve tissue distribution prediction accuracy thus enhancing the confidence in PBPK modeling outputs

Development of a Framework for Identifying Critical Input Parameters for Effective Pediatric PBPK/PBTK Modelling

Within a physiologically based pharmacokinetic (PBPK) model framework, virtual children are built based on known trajectories of anatomy and physiology across age and a compounds transfer within the body is defined by physicochemical and biochemical properties. Pediatric PBPK models have been used to derive doses for pediatric clinical trials and to assess risk of exposure to environmental chemicals. The identification of critical system- and compound-specific input parameters for pediatric PBPK modeling is crucial to applying this approach where pharmacokinetic (PK) data are limited. The objective of this study is to suggest a framework for the development of effective pediatric PBPK models by (i) identifying the most critical input parameters affecting the model precision as a means of targeting experimentation and by (ii) developing a workflow that combines available in silico prediction methods to estimate PK parameters in children. It is hypothesized that the framework for pediatric PBPK modeling will decrease the uncertainty associated with human health risk assessment in children. Pediatric PBPK models for 10 hepatically metabolized compounds were developed and their predictive performance was evaluated by comparing the predicted and observed PK values in children. Resonable prediction accuracy was demonstrated such that eighty-one percent of the comparisons between simulated and observed clearance values were within two-fold error. Through sensitivity analyses, the most important parameters for pediatric PBPK modeling were identified. It was found that protein binding and clearance parameters were important for pediatric PBPK models. In light of these findings, prediction methods of plasma protein binding and clearance in children were chosen as the main topics of this dissertation. When experimentally determined plasma protein binding information is not available, quantitative structure–property relationship (QSPR) models can be used to predict fraction unbound in plasma (fup) in humans. Three available QSPR models were evaluated. The most important chemical descriptors for predicting fup were lipophilicity, positive polar surface area, and the number of basic functional groups. It was found that the prediction of fup was the most uncertain for highly bound compounds. The next step was to evaluate adult-to-children scaling algorithms (ontogeny models) for fup. The predictive performance of 4 ontogeny models for albumin and 5 ontogeny models for alpha1-acid glycoprotein (AAG) were evaluated. Plasma protein concentrations vs. age profiles derived from non-linear equations (PK-Sim and Johnson et al.) were more in agreement with the observed levels than other models. Prediction accuracy of the ontogeny model depended on the appropriateness of the protein concentration vs. age profiles of ontogeny models particularly for highly bound compounds. For environmentally relevant compounds which are data-poor (e.g. information on physicochemical, toxicokinetic, and toxicological properties is not available), the prediction accuracy for fup prediction in children (fupchild) can be different than pharmaceuticals where experimental data such as fup in adults (fupadult) is often available. The prediction of fupchild for data-limited scenarios were evaluated with data-rich compounds such as pharmaceuticals as fupchild values are often available for those compounds. When QSPR-predicted fup in adult values were used as an input for predicting fupchild, over-predictions were observed for acids and neutrals with an average fold error (AFE) up to 8. The results indicated that an experimental determination of fup in adults was crucial. Two methods were proposed for predicting clearance (CL) in children from compound structure. The workflow utilizes QSPR models, protein binding ontogeny models and virtual pediatric individuals. Hepatic intrinsic CL, renal CL and fup in adults were estimated from a compound structure based on QSPR methods. Appropriate scaling methods were used to estimate CL in children. The QSPR-predicted CL values showed an over-prediction with geometric mean fold error values ranging from 1.9 to 3.29. When a predominant clearance pathway (e.g. hepatic metabolism or renal excretion) was predicted based on physicochemical properties of compounds and this information was used for CL prediction in children, the prediction accuracy was improved. The proposed workflow is considered to provide a reasonable estimation of clearance in pediatric population for human health risk assessment for data-sparse compounds. Prediction of dermal absorption in children is an important aspect in human health risk assessment. A pediatric dermal absorption model was developed by incorporating maturation functions into a MoBi implementation of the Dancik et al. 2013 skin permeation model. Adult models were first developed by optimizing key chemical specific parameters using the observed dermal absorption data in adults (e.g. in vitro permeation testing experimentation). For predicting dermal absorption in children, chemical-specific parameters in the model remained the same as in the adult model and age-dependent components of dermal absorption (e.g., skin layer thickness and hydration) were scaled as a function of age. This model can be used to predict dermal absorption in children by taking into account the physicochemical properties of the drug and the maturation of skin physiology and anatomy. In order to incorporate the information on the maturation of skin physiology and anatomy, extensive literature search was conducted. The predictive performance of the model was evaluated by comparing predicted and observed rate of dermal absorption for three compounds where IVPT data were available for neonates. The model described the trend of increased flux in neonates compared to adults. The predicted flux values were similar to the observed and predicted mean flux in neonates generally fell within the 90 percent prediction intervals. More IVPT data in children are required for model evaluation, however, the preliminary assessment with the limited set of data demonstrated favourable outcome. The studies in this dissertation evaluated computational methods that can be used to estimate pediatric PK for data-sparse compounds (e.g. environmentally relevant compounds). The proposed workflows and developed models for estimating important PK parameters in children in this dissertation are considered to be useful in decreasing uncertainties associated with PK in children estimation from compound structure for environmentally relevant compounds. Furthermore, the proposed models are physiologically relevant and these models will help risk assessors to make informed decisions for human health risk assessment in children

Prometheus: Inducing Fine-grained Evaluation Capability in Language Models

Recently, using a powerful proprietary Large Language Model (LLM) (e.g., GPT-4) as an evaluator for long-form responses has become the de facto standard. However, for practitioners with large-scale evaluation tasks and custom criteria in consideration (e.g., child-readability), using proprietary LLMs as an evaluator is unreliable due to the closed-source nature, uncontrolled versioning, and prohibitive costs. In this work, we propose Prometheus, a fully open-source LLM that is on par with GPT-4's evaluation capabilities when the appropriate reference materials (reference answer, score rubric) are accompanied. We first construct the Feedback Collection, a new dataset that consists of 1K fine-grained score rubrics, 20K instructions, and 100K responses and language feedback generated by GPT-4. Using the Feedback Collection, we train Prometheus, a 13B evaluator LLM that can assess any given long-form text based on customized score rubric provided by the user. Experimental results show that Prometheus scores a Pearson correlation of 0.897 with human evaluators when evaluating with 45 customized score rubrics, which is on par with GPT-4 (0.882), and greatly outperforms ChatGPT (0.392). Furthermore, measuring correlation with GPT-4 with 1222 customized score rubrics across four benchmarks (MT Bench, Vicuna Bench, Feedback Bench, Flask Eval) shows similar trends, bolstering Prometheus's capability as an evaluator LLM. Lastly, Prometheus achieves the highest accuracy on two human preference benchmarks (HHH Alignment & MT Bench Human Judgment) compared to open-sourced reward models explicitly trained on human preference datasets, highlighting its potential as an universal reward model. We open-source our code, dataset, and model at https://kaistai.github.io/prometheus/.Comment: ICLR 202

Reduced chronic restraint stress in mice overexpressing hyperactive proteasomes in the forebrain

While chronic restraint stress (CRS) results in depression-like behaviors possibly through oxidative stress in the brain, its molecular etiology and the development of therapeutic strategies remain elusive. Since oxidized proteins can be targeted by the ubiquitin-proteasome system, we investigated whether increased proteasome activity might affect the stress response in mice. Transgenic mice, expressing the N-terminally deleted version of α3 subunit (α3ΔN) of the proteasome, which has been shown to generate open-gated mutant proteasomes, in the forebrain were viable and fertile, but showed higher proteasome activity. After being challenged with CRS for 14 d, the mutant mice with hyperactive proteasomes showed significantly less immobility time in the forced swimming test compared with their wild-type littermates, suggesting that the α3ΔN transgenic mice are resistant to CRS. The accumulation of ER stress markers, such as polyubiquitin conjugates and phospho-IRE1α, was also significantly delayed in the hippocampus of the mutants. Notably, α3ΔN mice exhibited little deficits in other behavioral tasks, suggesting that stress resilience is likely due to the degradation of misfolded proteins by the open-gated proteasomes. These data strongly indicate that not only is the proteasome a critical modulator of stress response in vivo but also a possible therapeutic target for reducing chronic stress.This work was supported by grants from the National Research Foundation (2019R1A2B5B02069530, 2016M3C7A1913895 to M.J.L. 2017R1A6A3A11029936 to S.P., 2019R1A2C1005987 to J.H.L., and 2019R1A4A2001609 to Y.-S.L.), Korea Toray Science Foundation (800–20180524 to M.J.L.), and the Creative-Pioneering Researchers Program through Seoul National University (800–20160281 to M.J.L.)

The Draft Genome of an Octocoral, Dendronephthya gigantea

Coral reefs composed of stony corals are threatened by global marine environmental changes. However, soft coral communities of octocorallian species, appear more resilient. The genomes of several cnidarians species have been published, including from stony corals, sea anemones, and hydra. To fill the phylogenetic gap for octocoral species of cnidarians, we sequenced the octocoral, Dendronephthya gigantea, a nonsymbiotic soft coral, commonly known as the carnation coral. The D. gigantea genome size is similar to 276 Mb. A high-quality genome assembly was constructed from PacBio long reads (29.85 Gb with 108x coverage) and Illumina short paired-end reads (35.54 Gb with 128x coverage) resulting in the highest N50 value (1.4 Mb) reported thus far among cnidarian genomes. About 12% of the genome is repetitive elements and contained 28,879 predicted protein-coding genes. This gene set is composed of 94% complete BUSCO ortholog benchmark genes, which is the second highest value among the cnidarians, indicating high quality. Based on molecular phylogenetic analysis, octocoral and hexacoral divergence times were estimated at 544 MYA. There is a clear difference in Hox gene composition between these species: unlike hexacorals, the Antp superclass Evx gene was absent in D. gigantea. Here, we present the first genome assembly of a nonsymbiotic octocoral, D. gigantea to aid in the comparative genomic analysis of cnidarians, including stony and soft corals, both symbiotic and nonsymbiotic. The D. gigantea genome may also provide clues to mechanisms of differential coping between the soft and stony corals in response to scenarios of global warming

Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild-type primary glioblastoma

Background Although temozolomide (TMZ) has been used as a standard adjuvant chemotherapeutic agent for primary glioblastoma (GBM), treating isocitrate dehydrogenase wild-type (IDH-wt) cases remains challenging due to intrinsic and acquired drug resistance. Therefore, elucidation of the molecular mechanisms of TMZ resistance is critical for its precision application. Methods We stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy. Results In vitro TMZ sensitivity of patient-derived GSCs classified patients into groups with different survival outcomes (P = 1.12e−4 for progression-free survival (PFS) and 3.63e−4 for overall survival (OS)). Moreover, we found that elevated gene expression of EGR4, PAPPA, LRRC3, and ANXA3 was associated to intrinsic TMZ resistance. In addition, other features such as 5-aminolevulinic acid negative, mesenchymal/proneural expression subtypes, and hypermutation phenomena were prone to promote TMZ resistance. In contrast, concurrent copy-number-alteration in PTEN, EGFR, and CDKN2A/B was more frequent in TMZ-sensitive samples (Fishers exact P = 0.0102), subsequently consolidated by multi-sector sequencing analyses. Integrating all features, we trained a ML tool to segregate TMZ-resistant and sensitive groups. Notably, our method segregated IDH-wt GBM patients from The Cancer Genome Atlas (TCGA) into two groups with divergent survival outcomes (P = 4.58e−4 for PFS and 3.66e−4 for OS). Furthermore, we showed a highly heterogeneous TMZ-response pattern within each GBM patient usingin vitro TMZ screening and genomic characterization of multisector GSCs. Lastly, the prediction model that evaluates the TMZ efficacy for primary IDH-wt GBMs was developed into a webserver for public usage (http://www.wang-lab-hkust.com:3838/TMZEP) Conclusions We identified molecular characteristics associated to TMZ sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs

Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild-type primary glioblastoma

Background Although temozolomide (TMZ) has been used as a standard adjuvant chemotherapeutic agent for primary glioblastoma (GBM), treating isocitrate dehydrogenase wild-type (IDH-wt) cases remains challenging due to intrinsic and acquired drug resistance. Therefore, elucidation of the molecular mechanisms of TMZ resistance is critical for its precision application. Methods We stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy. Results In vitro TMZ sensitivity of patient-derived GSCs classified patients into groups with different survival outcomes (P = 1.12e−4 for progression-free survival (PFS) and 3.63e−4 for overall survival (OS)). Moreover, we found that elevated gene expression of EGR4, PAPPA, LRRC3, and ANXA3 was associated to intrinsic TMZ resistance. In addition, other features such as 5-aminolevulinic acid negative, mesenchymal/proneural expression subtypes, and hypermutation phenomena were prone to promote TMZ resistance. In contrast, concurrent copy-number-alteration in PTEN, EGFR, and CDKN2A/B was more frequent in TMZ-sensitive samples (Fisher’s exact P = 0.0102), subsequently consolidated by multi-sector sequencing analyses. Integrating all features, we trained a ML tool to segregate TMZ-resistant and sensitive groups. Notably, our method segregated IDH-wt GBM patients from The Cancer Genome Atlas (TCGA) into two groups with divergent survival outcomes (P = 4.58e−4 for PFS and 3.66e−4 for OS). Furthermore, we showed a highly heterogeneous TMZ-response pattern within each GBM patient using in vitro TMZ screening and genomic characterization of multisector GSCs. Lastly, the prediction model that evaluates the TMZ efficacy for primary IDH-wt GBMs was developed into a webserver for public usage ( http://www.wang-lab-hkust.com:3838/TMZEP ). Conclusions We identified molecular characteristics associated to TMZ sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs

Development of microcracks in granitic rock by liquid CO2 fracturing

Hydraulic fracturing by liquid carbon dioxide (LCO2) generates fracture and cracking patterns that vary from those generated by water injection. The use of LCO2 as a fracturing fluid can minimize water usage and potentially sequester CO2. In this study, hydraulic fracturing by LCO2 and water in a granitic specimen was performed, and the micro-scale characteristics of generated microcracks were investigated using an X-ray imaging technique and thin-section analysis. The results revealed that LCO2 and water injection produced different fracture characteristics. The injection of LCO2 with less viscosity and high compressibility required a greater fluid volume for fracture initiation to generate a lower breakdown pressure, and also generated higher crack-density zones located near the borehole hole possibly because of facilitated permeation amount into the rock matrix as compared to the water-injection case. In both the LCO2- and water-injection cases, the fractures developed along the rift cleavage plane, and an increase in microcrack density was observed in regions within 6 mm from the borehole. It was confirmed that the statistical and spatial distributions of developed microcracks were affected by the fracturing fluid and anisotropic properties of granitic rocks. The results of this study could be applied to fracturing that employs less water, CO2 sequestration, and recovery of geothermal energy

Deep learning for extracting micro-fracture: Pixel-level detection by convolutional neural network

Hydraulic stimulation has been a key technique in enhanced geothermal systems (EGS) and the recovery of unconventional hydrocarbon resources to artificially generate fractures in a rock formation. Previous experimental studies present that the pattern and aperture of generated fractures vary as the fracking pressure propagation. The recent development of three-dimensional X-ray computed tomography allows visualizing the fractures for further analysing the morphological features of fractures. However, the generated fracture consists of a few pixels (e.g., 1-3 pixels) so that the accurate and quantitative extract of micro-fracture is highly challenging. Also, the high-frequency noise around the fracture and the weak contrast across the fracture makes the application of conventional segmentation methods limited. In this study, we adopted an encoder-decoder network with a convolutional neural network (CNN) based on deep learning method for the fast and precise detection of micro-fractures. The conventional image processing methods fail to extract the continuous fractures and overestimate the fracture thickness and aperture values while the CNN-based approach successfully detects the barely seen fractures. The reconstruction of the 3D fracture surface and quantitative roughness analysis of fracture surfaces extracted by different methods enables comparison of sensitivity (or robustness) to noise between each method

Development and Evaluation of an In Silico Dermal Absorption Model Relevant for Children

The higher skin surface area to body weight ratio in children and the prematurity of skin in neonates may lead to higher chemical exposure as compared to adults. The objectives of this study were: (i) to provide a comprehensive review of the age-dependent anatomical and physiological changes in pediatric skin, and (ii) to construct and evaluate an age-dependent pediatric dermal absorption model. A comprehensive review was conducted to gather data quantifying the differences in the anatomy and physiology of child and adult skin. Maturation functions were developed for model parameters that were found to be age-dependent. A pediatric dermal absorption model was constructed by updating a MoBi implementation of the Dancik et al. 2013 skin permeation model with these maturation functions. Using a workflow for adult-to-child model extrapolation, the predictive performance of the model was evaluated by comparing its predicted rates of flux of diamorphine, phenobarbital and buprenorphine against experimental observations using neonatal skin. For diamorphine and phenobarbital, the model provided reasonable predictions. The ratios of predicted:observed flux in neonates for diamorphine ranged from 0.55 to 1.40. For phenobarbital, the ratios ranged from 0.93 to 1.26. For buprenorphine, the model showed acceptable predictive performance. Overall, the physiologically based pediatric dermal absorption model demonstrated satisfactory prediction accuracy. The prediction of dermal absorption in neonates using a model-based approach will be useful for both drug development and human health risk assessment