A Comparison of Google and ChatGPT for Automatic Generation of Health-related Multiple-choice Questions.

Critical to producing accessible content is an understanding of what characteristics affect understanding and comprehension. To answer this question, we are producing a large corpus of health-related texts with associated questions that can be read or listened to by study participants to measure the difficulty of the underlying content, which can later be used to better understand text difficulty and user comprehension. In this paper, we examine methods for automatically generating multiple-choice questions using Google's related questions and ChatGPT. Overall, we find both algorithms generate reasonable questions that are complementary; ChatGPT questions are more similar to the snippet while Google related-search questions have more lexical variation.

Optimizing peptide inhibitors of SARS-Cov-2 nsp10/nsp16 methyltransferase predicted through molecular simulation and machine learning.

Coronaviruses, including the recent pandemic strain SARS-Cov-2, use a multifunctional 2'-O-methyltransferase (2'-O-MTase) to restrict the host defense mechanism and to methylate RNA. The nonstructural protein 16 2'-O-MTase (nsp16) becomes active when nonstructural protein 10 (nsp10) and nsp16 interact. Novel peptide drugs have shown promise in the treatment of numerous diseases and new research has established that nsp10 derived peptides can disrupt viral methyltransferase activity via interaction of nsp16. This study had the goal of optimizing new analogous nsp10 peptides that have the ability to bind nsp16 with equal to or higher affinity than those naturally occurring. The following research demonstrates that in silico molecular simulations can shed light on peptide structures and predict the potential of new peptides to interrupt methyltransferase activity via the nsp10/nsp16 interface. The simulations suggest that misalignments at residues F68, H80, I81, D94, and Y96 or rotation at H80 abrogate MTase function. We develop a new set of peptides based on conserved regions of the nsp10 protein in the Coronaviridae species and test these to known MTase variant values. This results in the prediction that the H80R variant is a solid new candidate for potential new testing. We envision that this new lead is the beginning of a reputable foundation of a new computational method that combats coronaviruses and that is beneficial for new peptide drug development.

Active site prediction of phosphorylated SARS-CoV-2 N-Protein using molecular simulation.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) nucleocapsid protein (N-protein) is responsible for viral replication by assisting in viral RNA synthesis and attaching the viral genome to the replicase-transcriptase complex (RTC). Numerous studies suggested the N-protein as a drug target. However, the specific N-protein active sites for SARS-CoV-2 drug treatments are yet to be discovered. The purpose of this study was to determine active sites of the SARS-CoV-2 N-protein by identifying torsion angle classifiers for N-protein structural changes that correlated with the respective angle differences between the active and inactive N-protein. In the study, classifiers with a minimum accuracy of 80% determined from molecular simulation data were analyzed by Principal Component Analysis and cross-validated by Logistic Regression, Support Vector Machine, and Random Forest Classification. The ability of torsion angles ψ252 and φ375 to differentiate between phosphorylated and unphosphorylated structures suggested that residues 252 and 375 in the RNA binding domain might be important in N-protein activation. Furthermore, the φ and ψ angles of residue S189 correlated to a 90.7% structural determination accuracy. The key residues involved in the structural changes identified here might suggest possible important functional sites on the N-protein that could be the focus of further study to understand their potential as drug targets.

Machine learning-based prediction of drug and ligand binding in BCL-2 variants through molecular dynamics.

Venetoclax is a BH3 (BCL-2 Homology 3) mimetic used to treat leukemia and lymphoma by inhibiting the anti-apoptotic BCL-2 protein thereby promoting apoptosis of cancerous cells. Acquired resistance to Venetoclax via specific variants in BCL-2 is a major problem for the successful treatment of cancer patients. Replica exchange molecular dynamics (REMD) simulations combined with machine learning were used to define the average structure of variants in aqueous solution to predict changes in drug and ligand binding in BCL-2 variants. The variant structures all show shifts in residue positions that occlude the binding groove, and these are the primary contributors to drug resistance. Correspondingly, we established a method that can predict the severity of a variant as measured by the inhibitory constant (Ki) of Venetoclax by measuring the structure deviations to the binding cleft. In addition, we also applied machine learning to the phi and psi angles of the amino acid backbone to the ensemble of conformations that demonstrated a generalizable method for drug resistant predictions of BCL-2 proteins that elucidates changes where detailed understanding of the structure-function relationship is less clear.

Data Mining of Molecular Simulations Suggest Key Amino Acid Residues for Aggregation, Signaling and Drug Action.

Alzheimer's disease, the most common form of dementia, currently has no cure. There are only temporary treatments that reduce symptoms and the progression of the disease. Alzheimer's disease is characterized by the prevalence of plaques of aggregated amyloid ß (Aß) peptide. Recent treatments to prevent plaque formation have provided little to relieve disease symptoms. Although there have been numerous molecular simulation studies on the mechanisms of Aß aggregation, the signaling role has been less studied. In this study, a total of over 38,000 simulated structures, generated from molecular dynamics (MD) simulations, exploring different conformations of the Aß42 mutants and wild-type peptides were used to examine the relationship between Aß torsion angles and disease measures. Unique methods characterized the data set and pinpointed residues that were associated in aggregation and others associated with signaling. Machine learning techniques were applied to characterize the molecular simulation data and classify how much each residue influenced the predicted variant of Alzheimer's Disease. Orange3 data mining software provided the ability to use these techniques to generate tables and rank the data. The test and score module coupled with the confusion matrix module analyzed data with calculations of specificity and sensitivity. These methods evaluating frequency and rank allowed us to analyze and predict important residues associated with different phenotypic measures. This research has the potential to help understand which specific residues of Aß should be targeted for drug development.

Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations.

Distinct missense mutations in a specific gene have been associated with different diseases as well as differing severity of a disease. Current computational methods predict the potential pathogenicity of a missense variant but fail to differentiate between separate disease or severity phenotypes. We have developed a method to overcome this limitation by applying machine learning to features extracted from molecular dynamics simulations, creating a way to predict the effect of novel genetic variants in causing a disease, drug resistance, or another specific trait. As an example, we have applied this novel approach to variants in calmodulin associated with two distinct arrhythmias as well as two different neurodegenerative diseases caused by variants in amyloid-ß peptide. The new method successfully predicts the specific disease caused by a gene variant and ranks its severity with more accuracy than existing methods. We call this method molecular dynamics phenotype prediction model.

A Comparative Review of Chemotherapy-Induced Peripheral Neuropathy in In Vivo and In Vitro Models.

Chemotherapy-induced peripheral neuropathy (CIPN) is an adverse effect caused by several classes of widely used anticancer therapeutics. Chemotherapy-induced peripheral neuropathy frequently leads to dose reduction or discontinuation of chemotherapy regimens, and CIPN symptoms can persist long after completion of chemotherapy and severely diminish the quality of life of patients. Differences in the clinical presentation of CIPN by widely diverse classifications of anticancer agents have spawned multiple mechanistic hypotheses that seek to explain the pathogenesis of CIPN. Despite its clinical relevance, common occurrence, and extensive investigation, the pathophysiology of CIPN remains unclear. Furthermore, there is no unequivocal gold standard for the prevention and treatment of CIPN. Herein, we review in vivo and in vitro models of CIPN with a focus on histopathological changes and morphological features aimed at understanding the pathophysiology of CIPN and identify gaps requiring deeper exploration. An elucidation of the underlying mechanisms of CIPN is imperative to identify potential targets and approaches for prevention and treatment.

Targeting Glycolysis through Inhibition of Lactate Dehydrogenase Impairs Tumor Growth in Preclinical Models of Ewing Sarcoma.

Altered cellular metabolism, including an increased dependence on aerobic glycolysis, is a hallmark of cancer. Despite the fact that this observation was first made nearly a century ago, effective therapeutic targeting of glycolysis in cancer has remained elusive. One potentially promising approach involves targeting the glycolytic enzyme lactate dehydrogenase (LDH), which is overexpressed and plays a critical role in several cancers. Here, we used a novel class of LDH inhibitors to demonstrate, for the first time, that Ewing sarcoma cells are exquisitely sensitive to inhibition of LDH. EWS-FLI1, the oncogenic driver of Ewing sarcoma, regulated LDH A (LDHA) expression. Genetic depletion of LDHA inhibited proliferation of Ewing sarcoma cells and induced apoptosis, phenocopying pharmacologic inhibition of LDH. LDH inhibitors affected Ewing sarcoma cell viability both in vitro and in vivo by reducing glycolysis. Intravenous administration of LDH inhibitors resulted in the greatest intratumoral drug accumulation, inducing tumor cell death and reducing tumor growth. The major dose-limiting toxicity observed was hemolysis, indicating that a narrow therapeutic window exists for these compounds. Taken together, these data suggest that targeting glycolysis through inhibition of LDH should be further investigated as a potential therapeutic approach for cancers such as Ewing sarcoma that exhibit oncogene-dependent expression of LDH and increased glycolysis. SIGNIFICANCE: LDHA is a pharmacologically tractable EWS-FLI1 transcriptional target that regulates the glycolytic dependence of Ewing sarcoma.

Editor's Highlight: Multiparametric Image Analysis of Rat Dorsal Root Ganglion Cultures to Evaluate Peripheral Neuropathy-Inducing Chemotherapeutics.

Chemotherapy-induced peripheral neuropathy (CIPN) is a major, dose-limiting adverse effect experienced by cancer patients. Advancements in mechanism-based risk mitigation and effective treatments for CIPN can be aided by suitable in vitro assays. To this end, we developed a multiparametric morphology-centered rat dorsal root ganglion (DRG) assay. Morphologic alterations in subcellular structures of neurons and non-neurons were analyzed with an automated microscopy system. Stains for NeuN (a neuron-specific nuclear protein) and Tuj-1 (ß-III tubulin) were used to identify neuronal cell nuclei and neuronal cell bodies/neurites, respectively. Vimentin staining (a component of Schwann cell intermediate filaments) was used to label non-neuronal supporting cells. Nuclei that stained with DAPI, but lacked NeuN represented non-neuronal cells. Images were analyzed following 24 h of continuous exposure to CIPN-inducing agents and 72 h after drug removal to provide a dynamic measure of recovery from initial drug effects. Treatment with bortezomib, cisplatin, eribulin, paclitaxel or vincristine induced a dose-dependent loss of neurite/process areas, mimicking the 'dying back' degeneration of axons, a histopathological hallmark of clinical CIPN in vivo. The IC50 for neurite loss was within 3-fold of the maximal clinical exposure (Cmax) for all five CIPN-inducing drugs, but was >4- or ≥ 28-fold of the Cmax for 2 non-CIPN-inducing agents. Compound-specific effects, eg, neurite fragmentation by cisplatin or bortezomib and enlarged neuronal cell bodies by paclitaxel, were also observed. Collectively, these results support the use of a quantitative, morphologic evaluation and a DRG cell culture model to inform risk and examine mechanisms of CIPN.

Human CD34(+) progenitor hematopoiesis in liquid culture for in vitro assessment of drug-induced myelotoxicity.

Utilization of validated CFU-GM assays for myelotoxicity screening is hampered by its labor-intensive and low-throughput nature. Herein, we transformed the defined CFU-GM assay conditions and IC90 endpoint into a higher throughput format. Human CD34(+) hematopoietic progenitors were cultured in a 96-well plate for 14 days with the same cytokine (rhGM-CSF) used in the CFU-GM assay. Expansion and differentiation toward myeloid lineages were manifested by characteristic changes in nuclear and cytoplasmic morphology and by temporal expression patterns of CD34, CD11b and CD13 markers. Inhibition of CD34(+) cell myelopoiesis by 12 anticancer drugs known to induce myelotoxicity in the clinic was quantifiable using either general cytotoxicity endpoints (cell growth area or total nucleus count) or lineage specific readouts (count of cells expressing CD11b and/or CD13). The IC50 and IC90 values derived from the concentration-response curves of 14-day drug exposure in CD34(+) cell culture were highly correlated with those from the international validation study of the CFU-GM assay, demonstrating capability to assess general cytotoxicity, cell proliferation and myelopoiesis simultaneously. These results suggest that this human CD34(+) hematopoietic progenitor cell assay can be used as a direct replacement for the validated, low throughput CFU-GM assay, and could expand application of in vitro myelotoxicity testing.

Characterization of acute biliary hyperplasia in Fisher 344 rats administered the indole-3-carbinol analog, NSC-743380.

NSC-743380 (1-[(3-chlorophenyl)-methyl]-1H-indole-3-carbinol) is in early stages of development as an anticancer agent. Two metabolites reflect sequential conversion of the carbinol functionality to a carboxaldehyde and the major metabolite, 1-[(3-chlorophenyl)-methyl]-1H-indole-3-carboxylic acid. In an exploratory toxicity study in rats, NSC-743380 induced elevations in liver-associated serum enzymes and biliary hyperplasia. Biliary hyperplasia was observed 2 days after dosing orally for 2 consecutive days at 100mg/kg/day. Notably, hepatotoxicity and biliary hyperplasia were observed after oral administration of the parent compound, but not when major metabolites were administered. The toxicities of a structurally similar but pharmacologically inactive molecule and a structurally diverse molecule with a similar efficacy profile in killing cancer cells in vitro were compared to NSC-743380 to explore scaffold versus target-mediated toxicity. Following two oral doses of 100mg/kg/day given once daily on two consecutive days, the structurally unrelated active compound produced hepatic toxicity similar to NSC-743380. The structurally similar inactive compound did not, but, lower exposures were achieved. The weight of evidence implies that the hepatotoxicity associated with NSC-743380 is related to the anticancer activity of the parent molecule. Furthermore, because biliary hyperplasia represents an unmanageable and non-monitorable adverse effect in clinical settings, this model may provide an opportunity for investigators to use a short-duration study design to explore biomarkers of biliary hyperplasia.

Examining the protective role of ErbB2 modulation in human-induced pluripotent stem cell-derived cardiomyocytes.

Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are being used as an in vitro model system in cardiac biology and in drug discovery (e.g., cardiotoxicity testing). Qualification of these cells for use in mechanistic investigations will require detailed evaluations of cardiomyocyte signaling pathways and cellular responses. ErbB signaling and the ligand neuregulin play critical roles in survival and functional integrity of cardiac myocytes. As such, we sought to characterize the expression and activity of the ErbB family of receptors. Antibody microarray analysis performed on cell lysates derived from maturing hiPSC-CMs detected expression of ∼570 signaling proteins. EGFR/ErbB1, HER2/ErbB2, and ErbB4, but not ErbB3 receptors, of the epidermal growth factor receptor family were confirmed by Western blot. Activation of ErbB signaling by neuregulin-1ß (NRG, a natural ligand for ErbB4) and its modulation by trastuzumab (a monoclonal anti-ErbB2 antibody) and lapatinib (a small molecule ErbB2 tyrosine kinase inhibitor) were evaluated through assessing phosphorylation of AKT and Erk1/2, two major downstream kinases of ErbB signaling, using nanofluidic proteomic immunoassay. Downregulation of ErbB2 expression by siRNA silencing attenuated NRG-induced AKT and Erk1/2 phosphorylation. Activation of ErbB signaling with NRG, or inhibition with trastuzumab, alleviated or aggravated doxorubicin-induced cardiomyocyte damage, respectively, as assessed by a real-time cellular impedance analysis and ATP measurement. Collectively, these results support the expanded use of hiPSC-CMs to examine mechanisms of cardiotoxicity and support the value of using these cells in early assessments of cardiotoxicity or efficacy.