Photobiomodulation (PBM) therapy has been effectively used to relieve pain and inflammation and promote tissue healing and regeneration in a broad range of ailments. Prior work has focused on intracellular mitochondrial cytochrome c oxidase, while extracellular latent TGF-β1 activation had been noted. This work investigated the role of PBM-generated redox signaling and integration in normal oral keratinocytes, using Western blots and pathway-specific small molecule inhibitors. We observed that PBM primarily generates ROS intracellularly within mitochondria, which then diffuse extracellularly to activate latent TGF-β1. This activation triggers ATF-4 expression through both canonical (Smad3) and non-canonical (p38, ERK) TGF-β signaling pathways. We observed a critical role for NFκB as an essential integrator, coordinating these responses as evidenced by the loss of ATF-4 expression following NFκB inhibition (BAY II) after both PBM and TGF-β1 treatments. Proteomic pathway analysis revealed that PBM downregulates inflammatory and apoptotic pathways while activating stress-adaptive responses in the NFκB pathway. A core set of PBM-induced redox, NFκB, and TGF-β signaling targets was identified. These findings suggest that optimal PBM treatment responses require a coordinated action of multiple signaling pathways that optimize cellular adaptation to stress and promote tissue repair rather than protracted inflammation and cell death.

The purpose was to evaluate retrieval‐augmented generative (RAG) artificial intelligence (AI) methods for assessing the regulatory compliance of drug information and adherence to best practices in clinical trial protocols. Integrated systems containing RAG and large language model (LLM) components were employed to evaluate drug information and clinical trial protocols. The drug information for adalimumab, insulin glargine, atorvastatin calcium, sertraline, and alprazolam was evaluated for compliance with Food and Drug Administration (FDA) clinical pharmacology guidance for indications, use in specific populations, and warnings and precautions. The reasons for the withdrawal of rofecoxib, valdecoxib, and troglitazone were elicited. The clinical trial protocol evaluation system was used to assess a Phase‐2a clinical trial protocol of Rifafour in tuberculosis with the FDA E9 and E9 (R1) guidance documents. The RAG system correctly identified the indications, use in specific populations, and warnings and precautions for adalimumab, insulin glargine, atorvastatin calcium, sertraline, and alprazolam. The drug information was evaluated against the requirements in the guidance documents, confirming compliance when present and providing explanations for deficiencies. The causes underlying the withdrawal of rofecoxib, valdecoxib, and troglitazone were explained. The clinical protocol summary included study design, population definitions, treatments, dose levels, and route of administration. The summary of the statistical analysis plan included primary/secondary endpoints, statistical tests, pharmacokinetic parameters, and handling of missing data and outliers. The findings aligned with manual protocol reviews. RAG‐based AI methods can improve the usefulness of LLMs in document‐restricted settings and are a promising approach for evaluating the compliance of clinical pharmacology documents.

The purpose was to evaluate the utility of large language models (LLMs) for reviewing the statistical analysis plan (SAP) and pharmacokinetics–pharmacodynamics (PK–PD) components of clinical trial protocols. Clinical trial protocols and SAPs were obtained from clinicaltrials.gov for a testbed of 15 small‐molecule drugs, biologics, and global antibiotic and public health interventions. The GPT‐4o (ChatGPT) LLM was used to elicit study design attributes, relevant guidelines, and detailed SAP evaluations with prompts engineered to the persona of a regulatory expert. The SAP methodology was assessed against the Food and Drug Administration’s (FDA) E9 Statistical Principles for Clinical Trials guidance. The SAP evaluation outputs were assessed in post hoc analyses with ChatGPT and Grok, based on a rubric that evaluated the accuracy of primary outcome identification, the correctness of statistical methodology, compliance with the FDA E9 guidance, and clinical interpretability. PK–PD analysis plans were assessed on the accuracy of PK–PD objectives and measures and PK analysis methods. ChatGPT accurately identified the disease, intervention, and comparator groups for all trials, as well as the study sample size for 14 out of 15 trials. The most frequently cited guidelines were the FDA’s E9 guidance for SAP and the FDA Guidance for Industry: Population Pharmacokinetics for PK–PD. ChatGPT outputs of the SAP and PK–PD analysis plans were clear and organized, demonstrating a satisfactory ability to extract and summarize technical details; some limitations in contextual accuracy were observed. LLMs can be effective tools for assessing the SAP, PK–PD, and other aspects of clinical trial protocol reviews.

Metabolic dysfunction‐associated fatty liver disease causes hepatic fat accumulation (steatosis) and fibrosis, which can be measured with ultrasound elastography imaging. The dependencies of elastography‐derived hepatic steatosis and fibrosis measures with chronic inflammation, disease states, and physiological determinants of drug dosing (PDODD) were assessed. Liver elastography data for n = 5494 participants (50% female, 12–80 years) were obtained from the National Health and Nutrition Examination Survey. Controlled attenuation parameter (CAP) and median liver stiffness (LSM) elastography metrics were used to assess steatosis and fibrosis, and their associations with over 50 key organ systems, disease, and PDODD biomarkers were evaluated with statistical regression, ensemble, and Bayesian learning methods. CAP and LSM increased with age and were greater in males, active liver disease, active hepatitis C, and diabetes or prediabetes. LSM was greater in the presence of congestive heart failure and dialysis. The inflammatory markers C‐reactive protein (CRP) and ferritin, body surface area, and hepatic R ‐value were greater in steatosis and fibrosis. Plasma volume, neutrophil, red blood cell, and platelet counts were greater in steatosis. Drug‐induced liver injury index was lower in steatosis and greater in fibrosis. Albumin levels and platelet counts were lower, but the urine albumin‐to‐creatine ratio was greater in fibrosis. Ensemble learning identified interactions among BMI, age, CRP, ferritin, and liver enzymes contributing to steatosis and fibrosis. Bayesian networks were used to identify directed acyclic graph structures for steatosis and fibrosis. Elastography‐derived measures may be useful for individualizing dosing regimens in the presence of metabolic comorbidities presenting dose‐selection challenges.

Age and aging are important predictors of health status, disease progression, drug kinetics, and effects. The purpose was to develop ensemble learning‐based physiological age (PA) models for evaluating drug metabolism. National Health and Nutrition Examination Survey (NHANES) data were modeled with ensemble learning to obtain two PA models, PA‐M1 and PA‐M2. PA‐M1 included body composition, blood and urine biomarkers, and disease variables as predictors. PA‐M2 had blood and urine‐derived variables as predictors. Activity phenotypes for cytochrome‐P450 (CYP) CYP2E1, CYP1A2, CYP2A6, xanthine oxidase (XO), and N‐acetyltransferase‐2 (NAT‐2) and telomere attrition were assessed. Bayesian networks were used to obtain mechanistic systems pharmacology model structures for PA. The study included n = 22,307 NHANES participants (51.5% female, mean age 46.0 years, range: 18–79 years). The PA‐M1 and PA‐M2 distributions had greater dispersion across age strata with a right skew for younger age strata and a left skew for older age strata. There was no evidence of algorithmic bias based on sex or race/ethnicity. Klotho, lean body mass, glycohemoglobin, and systolic blood pressure were the top four predictors for PA‐M1. Glycohemoglobin, serum creatinine, total cholesterol, and urine creatinine were the top four predictors for PA‐M2. The models also performed satisfactorily in independent validation. Model‐predicted PA was associated with CYP2E1, CYP1A2, CYP2A6, XO, and NAT‐2 activity. Telomere attrition was associated with greater PA‐M1 and PA‐M2. Ensemble learning models provide robust assessments of PA from easily obtained blood and urine biomarkers. PA is associated with Phase I drug‐metabolizing enzyme phenotypes.