Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine.

The COVID-19 pandemic has presented an unprecedented challenge to the healthcare system. Identifying the genomics and clinical biomarkers for effective patient stratification and management is critical to controlling the spread of the disease. Omics datasets provide a wealth of information that can aid in understanding the underlying molecular mechanisms of COVID-19 and identifying potential biomarkers for patient stratification. Artificial intelligence (AI) and machine learning (ML) algorithms have been increasingly used to analyze large-scale omics and clinical datasets for patient stratification. In this manuscript, we demonstrate the recent advances and predictive accuracies in AI- and ML-based patient stratification modeling linking omics and clinical biomarker datasets, focusing on COVID-19 patients. Our ML model not only demonstrates that clinical features are enough of an indicator of COVID-19 severity and survival, but also infers what clinical features are more impactful, which makes our approach a useful guide for clinicians for prioritization best-fit therapeutics for a given cohort of patients. Moreover, with weighted gene network analysis, we are able to provide insights into gene networks that have a significant association with COVID-19 severity and clinical features. Finally, we have demonstrated the importance of clinical biomarkers in identifying high-risk patients and predicting disease progression.

In Silico Development of Combinatorial Therapeutic Approaches Targeting Key Signaling Pathways in Metabolic Syndrome.

PURPOSE: Dysregulations of key signaling pathways in metabolic syndrome are multifactorial, eventually leading to cardiovascular events. Hyperglycemia in conjunction with dyslipidemia induces insulin resistance and provokes release of proinflammatory cytokines resulting in chronic inflammation, accelerated lipid peroxidation with further development of atherosclerotic alterations and diabetes. We have proposed a novel combinatorial approach using FDA approved compounds targeting IL-17a and DPP4 to ameliorate a significant portion of the clustered clinical risks in patients with metabolic syndrome. In our current research we have modeled the outcomes of metabolic syndrome treatment using two distinct drug classes. METHODS: Targets were chosen based on the clustered clinical risks in metabolic syndrome: dyslipidemia, insulin resistance, impaired glucose control, and chronic inflammation. Drug development platform, BIOiSIM™, was used to narrow down two different drug classes with distinct modes of action and modalities. Pharmacokinetic and pharmacodynamic profiles of the most promising drugs were modeling showing predicted outcomes of combinatorial therapeutic interventions. RESULTS: Preliminary studies demonstrated that the most promising drugs belong to DPP-4 inhibitors and IL-17A inhibitors. Evogliptin was chosen to be a candidate for regulating glucose control with long term collateral benefit of weight loss and improved lipid profiles. Secukinumab, an IL-17A sequestering agent used in treating psoriasis, was selected as a repurposed candidate to address the sequential inflammatory disorders that follow the first metabolic insult. CONCLUSIONS: Our analysis suggests this novel combinatorial therapeutic approach inducing DPP4 and Il-17a suppression has a high likelihood of ameliorating a significant portion of the clustered clinical risk in metabolic syndrome.

Bioactive Compounds with Antiglioma Activity from Marine Species.

The search for new chemical compounds with antitumor pharmacological activity is a necessary process for creating more effective drugs for each specific malignancy type. This review presents the outcomes of screening studies of natural compounds with high anti-glioma activity. Despite significant advances in cancer therapy, there are still some tumors currently considered completely incurable including brain gliomas. This review covers the main problems of the glioma chemotherapy including drug resistance, side effects of common anti-glioma drugs, and genetic diversity of brain tumors. The main emphasis is made on the characterization of natural compounds isolated from marine organisms because taxonomic diversity of organisms in seawaters significantly exceeds that of terrestrial species. Thus, we should expect greater chemical diversity of marine compounds and greater likelihood of finding effective molecules with antiglioma activity. The review covers at least 15 classes of organic compounds with their chemical formulas provided as well as semi-inhibitory concentrations, mechanisms of action, and pharmacokinetic profiles. In conclusion, the analysis of the taxonomic diversity of marine species containing bioactives with antiglioma activity is performed noting cytotoxicity indicators and to the tumor cells in comparison with similar indicators of antitumor agents approved for clinical use as antiglioblastoma chemotherapeutics.

A hybrid modeling approach for assessing mechanistic models of small molecule partitioning in vivo using a machine learning-integrated modeling platform.

Prediction of the first-in-human dosing regimens is a critical step in drug development and requires accurate quantitation of drug distribution. Traditional in vivo studies used to characterize clinical candidate's volume of distribution are error-prone, time- and cost-intensive and lack reproducibility in clinical settings. The paper demonstrates how a computational platform integrating machine learning optimization with mechanistic modeling can be used to simulate compound plasma concentration profile and predict tissue-plasma partition coefficients with high accuracy by varying the lipophilicity descriptor logP. The approach applied to chemically diverse small molecules resulted in comparable geometric mean fold-errors of 1.50 and 1.63 in pharmacokinetic outputs for direct tissue:plasma partition and hybrid logP optimization, with the latter enabling prediction of tissue permeation that can be used to guide toxicity and efficacy dosing in human subjects. The optimization simulations required to achieve these results were parallelized on the AWS cloud and generated outputs in under 5 h. Accuracy, speed, and scalability of the framework indicate that it can be used to assess the relevance of other mechanistic relationships implicated in pharmacokinetic-pharmacodynamic phenomena with a lower risk of overfitting datasets and generate large database of physiologically-relevant drug disposition for further integration with machine learning models.

Accelerated Repurposing and Drug Development of Pulmonary Hypertension Therapies for COVID-19 Treatment Using an AI-Integrated Biosimulation Platform.

The COVID-19 pandemic has reached over 100 million worldwide. Due to the multi-targeted nature of the virus, it is clear that drugs providing anti-COVID-19 effects need to be developed at an accelerated rate, and a combinatorial approach may stand to be more successful than a single drug therapy. Among several targets and pathways that are under investigation, the renin-angiotensin system (RAS) and specifically angiotensin-converting enzyme (ACE), and Ca2+-mediated SARS-CoV-2 cellular entry and replication are noteworthy. A combination of ACE inhibitors and calcium channel blockers (CCBs), a critical line of therapy for pulmonary hypertension, has shown therapeutic relevance in COVID-19 when investigated independently. To that end, we conducted in silico modeling using BIOiSIM, an AI-integrated mechanistic modeling platform by utilizing known preclinical in vitro and in vivo datasets to accurately simulate systemic therapy disposition and site-of-action penetration of the CCBs and ACEi compounds to tissues implicated in COVID-19 pathogenesis.

Effects of Magnesium, Calcium, and Aluminum Chelation on Fluoroquinolone Absorption Rate and Bioavailability: A Computational Study.

Fluoroquinolones (FQs) are a widespread class of broad-spectrum antibiotics prescribed as a first line of defense, and, in some cases, as the only treatment against bacterial infection. However, when administered orally, reduced absorption and bioavailability can occur due to chelation in the gastrointestinal tract (GIT) with multivalent metal cations acquired from diet, coadministered compounds (sucralfate, didanosine), or drug formulation. Predicting the extent to which this interaction reduces in vivo antibiotic absorption and systemic exposure remains desirable yet challenging. In this study, we focus on quinolone interactions with magnesium, calcium and aluminum as found in dietary supplements, antacids (Maalox) orally administered therapies (sucralfate, didanosine). The effect of FQ-metal complexation on absorption rate was investigated through a combined molecular and pharmacokinetic (PK) modeling study. Quantum mechanical calculations elucidated FQ-metal binding energies, which were leveraged to predict the magnitude of reduced bioavailability via a quantitative structure-property relationship (QSPR). This work will help inform clinical FQ formulation design, alert to possible dietary effects, and shed light on drug-drug interactions resulting from coadministration at an earlier stage in the drug development pipeline.

In Silico Simulation of the Systemic Drug Exposure Following the Topical Application of Opioid Analgesics in Patients with Cutaneous Lesions.

The use of opioid analgesics in treating severe pain is frequently associated with putative adverse effects in humans. Topical agents that are shown to have high efficacy with a favorable safety profile in clinical settings are great alternatives for pain management of multimodal analgesia. However, the risk of side effects induced by transdermal absorption and systemic exposure is of great concern as they are challenging to predict. The present study aimed to use "BIOiSIM" an artificial intelligence-integrated biosimulation platform to predict the transdermal disposition of opioid analgesics. The model successfully predicted their exposure following the topical application of central opioid agonist buprenorphine and peripheral agonist oxycodone in healthy human subjects with simulation of intra-skin exposure in subjects with burns and pressure wounds. The predicted plasma levels of analgesics were used to evaluate the safety of the therapeutic pain control in patients with the dermal structural impairments caused by acute (burns) or chronic cutaneous lesions (pressure wounds) with topical opioid analgesics.

Hirsutanol A Attenuates Lipopolysaccharide-Mediated Matrix Metalloproteinase 9 Expression and Cytokines Production and Improves Endotoxemia-Induced Acute Sickness Behavior and Acute Lung Injury.

Activated human monocytes/macrophages, which increase the levels of matrix metalloproteinases (MMPs) and pro-inflammatory cytokines, are the essential mechanisms for the progression of sepsis. In the present study, we determined the functions and mechanisms of hirsutanolA (HA), which is isolated from the red alga-derived marine fungus Chondrostereum sp. NTOU4196, on the production of pro-inflammatory mediators produced from lipopolysaccharide (LPS)-treated THP-1 cells. Our results showed that HA suppressed LPS-triggered MMP-9-mediated gelatinolysis and expression of protein and mRNA in a concentration-dependent manner without effects on TIMP-1 activity. Also, HA significantly attenuated the levels of TNF-α, IL-6, and IL-1ß from LPS-treated THP-1 cells. Moreover, HA significantly inhibited LPS-mediated STAT3 (Tyr705) phosphorylation, IκBα degradation and ERK1/2 activation in THP-1 cells. In an LPS-induced endotoxemia mouse model, studies indicated that HA pretreatment improved endotoxemia-induced acute sickness behavior, including acute motor deficits and anxiety-like behavior. HA also attenuated LPS-induced phospho-STAT3 and pro-MMP-9 activity in the hippocampus. Notably, HA reduced pathologic lung injury features, including interstitial tissue edema, infiltration of inflammatory cells and alveolar collapse. Likewise, HA suppressed the induction of phospho-STAT3 and pro-MMP-9 in lung tissues. In conclusion, our results provide pharmacological evidence that HA could be a useful agent for treating inflammatory diseases, including sepsis.

Lead-binding capacity of calcium pectates with different molecular weight.

Nowadays, heavy metal contamination of environment is considered as a serious threat to public health because of toxicity of these pollutants and the lack of effective materials with metal-binding properties. Some biopolymers such as pectins were proposed for removal of metal ions from industrial water disposals. Chemical structure of pectins is quite variable and substantially affects their metal binding properties. In this work, relationship between molecular weight and Pb(II)-binding capacity of calcium pectates was investigated in a batch sorption system. The results showed that all pectate samples are able to form complexes with Pb(II) ions. The effects of contact time, pH of the media and equilibrium metal concentration on metal-binding process were tested in experiments. The equilibrium time min required for uptake of Pb(II) by pectate compounds was found to be 60min. Langmuir and Freundlich models were applied for description of interactions between pectates and metal ions. Binding capacity of low molecular pectate was highest among all the samples tested. Langmuir model was figured out to be the best fit within the whole range of pH values. These results demonstrate that calcium pectate with low molecular weight is more promising agent for elimination of Pb(II) ions from contaminated wastewaters.

Removal of the metal ions from aqueous solutions by nanoscaled low molecular pectin isolated from seagrass Phyllospadix iwatensis.

Pectins from sea grasses are considered as promising substances with pronounced metal-binding activity. Due to the high molecular weight and heterogeneous structure, the use of pectins for removal of metal ions is difficult. Technology of directed pectin degradation was developed and homogenous degraded nanoscaled pectin polymers were synthesized. Experimental samples of degraded pectin isolated from Phyllospadix iwatensis were tested for their metal binding activity in comparison with native pectin from this seagrass and commercial citrus pectin. The metal uptake of all pectin compounds was highest within the pH range from 4.0 to 6.0. The Langmuir, Freundlich and BET sorption models were applied to describe the isotherms and constants. Results showed that depolymerized pectin exerts highest lead and cadmium binding activity with pronounced affinity. All pectin compounds were suggested to be favorable sorbents. Therefore, it can be concluded that degraded pectin is a prospective material for creation of metal-removing water treatment systems.