An AI Approach to Generating MIDD Assets Across the Drug Development Continuum.

Model-informed drug development involves developing and applying exposure-based, biological, and statistical models derived from preclinical and clinical data sources to inform drug development and decision-making. Discrete models are generated from individual experiments resulting in a single model expression that is utilized to inform a single stage-gate decision. Other model types provide a more holistic view of disease biology and potentially disease progression depending on the appropriateness of the underlying data sources for that purpose.

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.

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.

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.

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.

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 Ca-mediated SARS-CoV-2 cellular entry and replication are noteworthy.

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.

Driving success in personalized medicine through AI-enabled computational modeling.

The development of successful drugs is expensive and time-consuming because of high clinical attrition rates. This is caused partially by the rupture seen in the translatability of the drug from the bench to the clinic in the context of personalized medicine. Artificial intelligence (AI)-driven platforms integrated with mechanistic modeling have become instrumental in accelerating the drug development process by leveraging data ubiquitously across the various phases.

Molecular Simulation and Statistical Learning Methods toward Predicting Drug-Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design.

Amorphous solid dispersions (ASDs) have emerged as widespread formulations for drug delivery of poorly soluble active pharmaceutical ingredients (APIs). Predicting the API solubility with various carriers in the API-carrier mixture and the principal API-carrier non-bonding interactions are critical factors for rational drug development and formulation decisions. Experimental determination of these interactions, solubility, and dissolution mechanisms is time-consuming, costly, and reliant on trial and error.

Scalable in silico Simulation of Transdermal Drug Permeability: Application of BIOiSIM Platform.

Introduction: Transdermal drug delivery is gaining popularity as an alternative to traditional routes of administration. It can increase patient compliance because of its painless and noninvasive nature, aid compounds in bypassing presystemic metabolic effects, and reduce the likelihood of adverse effects through decreased systemic exposure. In silico physiological modeling is critical to predicting dermal exposure for a therapeutic and assessing the impact of different formulations on transdermal disposition.

Entering the era of computationally driven drug development.

Historically, failure rates in drug development are high; increased sophistication and investment throughout the process has shifted the reasons for attrition, but the overall success rates have remained stubbornly and consistently low. Only 8% of new entities entering clinical testing gain regulatory approval, indicating that significant obstacles still exist for efficient therapeutic development. The continued high failure rate can be partially attributed to the inability to link drug exposure with the magnitude of observed safety and efficacy-related pharmacodynamic (PD) responses; frequently, this is a result of nonclinical models exhibiting poor prediction of human outcomes across a wide range of disease conditions, resulting in faulty evaluation of drug toxicology and efficacy.

CRISPR/Cas9-Based Positive Screens for Cancer-Related Traits.

Since the advent of large-scale, detailed descriptive cancer genomics studies at the beginning of the century, such as The Cancer Genome Atlas (TCGA), labs around the world have been working to make this data useful. Data like these can be made more useful by comparison with complementary functional genomic data. One new example is the application of CRISPR/Cas9-based library screening for cancer-related traits in cell lines.

Comparative Transcriptome Analysis Quantifies Immune Cell Transcript Levels, Metastatic Progression, and Survival in Osteosarcoma.

Overall survival of patients with osteosarcoma (OS) has improved little in the past three decades, and better models for study are needed. OS is common in large dog breeds and is genetically inducible in mice, making the disease ideal for comparative genomic analyses across species. Understanding the level of conservation of intertumor transcriptional variation across species and how it is associated with progression to metastasis will enable us to more efficiently develop effective strategies to manage OS and to improve therapy.

Slit-Robo GTPase-Activating Protein 2 as a metastasis suppressor in osteosarcoma.

Osteosarcoma is the most common primary bone tumor, with metastatic disease responsible for most treatment failure and patient death. A forward genetic screen utilizing Sleeping Beauty mutagenesis in mice previously identified potential genetic drivers of osteosarcoma metastasis, including Slit-Robo GTPase-Activating Protein 2 (Srgap2). This study evaluates the potential role of SRGAP2 in metastases-associated properties of osteosarcoma cell lines through Srgap2 knockout via the CRISPR/Cas9 nuclease system and conditional overexpression in the murine osteosarcoma cell lines K12 and K7M2.

Imprinting defects at human 14q32 locus alters gene expression and is associated with the pathobiology of osteosarcoma.

Osteosarcoma is the most common primary bone malignancy affecting children and adolescents. Although several genetic predisposing conditions have been associated with osteosarcoma, our understanding of its pathobiology is rather limited. Here we show that, first, an imprinting defect at human 14q32-locus is highly prevalent (87%) and specifically associated with osteosarcoma patients < 30 years of age.

Understanding the Osteosarcoma Pathobiology: A Comparative Oncology Approach.

Osteosarcoma is an aggressive primary bone tumor in humans and is among the most common cancer afflicting dogs. Despite surgical advancements and intensification of chemo- and targeted therapies, the survival outcome for osteosarcoma patients is, as of yet, suboptimal. The presence of metastatic disease at diagnosis or its recurrence after initial therapy is a major factor for the poor outcomes.

Aberrant Retinoblastoma (RB)-E2F Transcriptional Regulation Defines Molecular Phenotypes of Osteosarcoma.

We previously identified two distinct molecular subtypes of osteosarcoma through gene expression profiling. These subtypes are associated with distinct tumor behavior and clinical outcomes. Here, we describe mechanisms that give rise to these molecular subtypes.

MicroRNAs as potential target in human bone and soft tissue sarcoma therapeutics.

Sarcomas are highly aggressive heterogeneous tumors that are mesenchymal in origin. There have been vast advancements on identifying diagnostic markers for sarcomas including chromosomal translocations, but very little progress has been made to identify targeted therapies against them. The tumor heterogeneity, genetic complexity and the lack of drug studies make it challenging to recognize the potential targets and also accounts for the inadequate treatments in sarcomas.

Characterization of Clostridium difficile isolates from human fecal samples and retail meat from Pennsylvania.

A study was conducted to determine the prevalence of Clostridium difficile and characterize C. difficile isolates from human stool and retail grocery meat samples. Human stool samples (n=317) were obtained from a clinical laboratory and meat samples (n=303) were collected from 8 retail grocery stores from October 2011 through September 2012 from Centre County of Pennsylvania and were examined for C.

White button mushrooms increase microbial diversity and accelerate the resolution of Citrobacter rodentium infection in mice.

The effect of feeding C57BL/6 mice white button (WB) mushrooms or control (CTRL) diets for 6 wk was determined on the bacterial microflora, urinary metabolome, and resistance to a gastrointestinal (GI) pathogen. Feeding mice a diet containing 1 g WB mushrooms/100 g diet resulted in changes in the microflora that were evident at 2 wk and stabilized after 4 wk of WB feeding. Compared with CTRL-fed mice, WB feeding (1 g/100 g diet) increased the diversity of the microflora and reduced potentially pathogenic (e.