Nurturing diversity and inclusion in AI in Biomedicine through a virtual summer program for high school students.

Artificial Intelligence (AI) has the power to improve our lives through a wide variety of applications, many of which fall into the healthcare space; however, a lack of diversity is contributing to limitations in how broadly AI can help people. The UCSF AI4ALL program was established in 2019 to address this issue by targeting high school students from underrepresented backgrounds in AI, giving them a chance to learn about AI with a focus on biomedicine, and promoting diversity and inclusion. In 2020, the UCSF AI4ALL three-week program was held entirely online due to the COVID-19 pandemic. Thus, students participated virtually to gain experience with AI, interact with diverse role models in AI, and learn about advancing health through AI. Specifically, they attended lectures in coding and AI, received an in-depth research experience through hands-on projects exploring COVID-19, and engaged in mentoring and personal development sessions with faculty, researchers, industry professionals, and undergraduate and graduate students, many of whom were women and from underrepresented racial and ethnic backgrounds. At the conclusion of the program, the students presented the results of their research projects at the final symposium. Comparison of pre- and post-program survey responses from students demonstrated that after the program, significantly more students were familiar with how to work with data and to evaluate and apply machine learning algorithms. There were also nominally significant increases in the students' knowing people in AI from historically underrepresented groups, feeling confident in discussing AI, and being aware of careers in AI. We found that we were able to engage young students in AI via our online training program and nurture greater diversity in AI. This work can guide AI training programs aspiring to engage and educate students entirely online, and motivate people in AI to strive towards increasing diversity and inclusion in this field.

Bacterial metabolism rescues the inhibition of intestinal drug absorption by food and drug additives.

Food and drug products contain diverse and abundant small-molecule additives (excipients) with unclear impacts on human physiology, drug safety, and response. Here, we evaluate their potential impact on intestinal drug absorption. By screening 136 unique compounds for inhibition of the key intestinal transporter OATP2B1 we identified and validated 24 potent OATP2B1 inhibitors, characterized by higher molecular weight and hydrophobicity compared to poor or noninhibitors. OATP2B1 inhibitors were also enriched for dyes, including 8 azo (R-N=N-R') dyes. Pharmacokinetic studies in mice confirmed that FD&C Red No. 40, a common azo dye excipient and a potent inhibitor of OATP2B1, decreased the plasma level of the OATP2B1 substrate fexofenadine, suggesting that FD&C Red No. 40 has the potential to block drug absorption through OATP2B1 inhibition in vivo. However, the gut microbiomes of multiple unrelated healthy individuals as well as diverse human gut bacterial isolates were capable of inactivating the identified azo dye excipients, producing metabolites that no longer inhibit OATP2B1 transport. These results support a beneficial role for the microbiome in limiting the unintended effects of food and drug additives in the intestine and provide a framework for the data-driven selection of excipients. Furthermore, the ubiquity and genetic diversity of gut bacterial azoreductases coupled to experiments in conventionally raised and gnotobiotic mice suggest that variations in gut microbial community structure may be less important to consider relative to the high concentrations of azo dyes in food products, which have the potential to saturate gut bacterial enzymatic activity.

Proteomic Profiling Reveals Age-Related Changes in Transporter Proteins in the Human Blood-Brain Barrier.

The Blood-Brain Barrier (BBB) is a crucial, selective barrier that regulates the entry of molecules including nutrients, environmental toxins, and therapeutic medications into the brain. This function relies heavily on brain endothelial cell proteins, particularly transporters and tight junction proteins. The BBB continues to develop postnatally, adapting its selective barrier function across different developmental phases, and alters with aging and disease. Here we present a global proteomics analysis focused on the ontogeny and aging of proteins in human brain microvessels (BMVs), predominantly composed of brain endothelial cells. Our proteomic profiling quantified 6,223 proteins and revealed possible age-related alteration in BBB permeability due to basement membrane component changes through the early developmental stage and age-dependent changes in transporter expression. Notable changes in expression levels were observed with development and age in nutrient transporters and transporters that play critical roles in drug disposition. This research 1) provides important information on the mechanisms that drive changes in the metabolic content of the brain with age and 2) enables the creation of physiologically based pharmacokinetic models for CNS drug distribution across different life stages.

Applications of Advanced Natural Language Processing for Clinical Pharmacology.

Natural language processing (NLP) is a branch of artificial intelligence, which combines computational linguistics, machine learning, and deep learning models to process human language. Although there is a surge in NLP usage across various industries in recent years, NLP has not been widely evaluated and utilized to support drug development. To demonstrate how advanced NLP can expedite the extraction and analyses of information to help address clinical pharmacology questions, inform clinical trial designs, and support drug development, three use cases are described in this article: (1) dose optimization strategy in oncology, (2) common covariates on pharmacokinetic (PK) parameters in oncology, and (3) physiologically-based PK (PBPK) analyses for regulatory review and product label. The NLP workflow includes (1) preparation of source files, (2) NLP model building, and (3) automation of data extraction. The Clinical Pharmacology and Biopharmaceutics Summary Basis of Approval (SBA) documents, US package inserts (USPI), and approval letters from the US Food and Drug Administration (FDA) were used as our source data. As demonstrated in the three example use cases, advanced NLP can expedite the extraction and analyses of large amounts of information from regulatory review documents to help address important clinical pharmacology questions. Although this has not been adopted widely, integrating advanced NLP into the clinical pharmacology workflow can increase efficiency in extracting impactful information to advance drug development.

Prevalence and utility of pharmacokinetic data in preclinical studies of mRNA cancer vaccines.

In this brief report, we provide insights into current practices in preclinical messenger RNA (mRNA) cancer vaccine characterization. To enable a more automated and thorough survey of mRNA cancer vaccine data in the literature, we implemented natural language processing to mine abstracts from PubMed followed by annotation of the selected articles. Through this analysis we identified a gap in the literature wherein pharmacokinetic (PK) characterization is not reported in mRNA cancer vaccine-focused articles. As a result, the field is unable to evaluate and discuss cross-study PK and pharmacodynamic (PD) relationships nor the translation of these relationships from preclinical species to humans. As the field of mRNA cancer vaccines is rapidly evolving, there is value in expanding our understanding of preclinical PK/PD relationships and how they relate to PK/PD in humans.

Applying Natural Language Processing to ClinicalTrials.gov: mRNA cancer vaccine case study.

Recently, biotechnology and pharmaceutical industries have made strides to adopt and implement Natural Language Processing (NLP) to address challenges faced when extracting and synthesizing high volumes of information found in unstructured and semistructured text. Here we present, and provide a summary of the findings from, a use case where NLP and text mining methodologies were used to extract clinical trial data from ClinicalTrials.gov for mRNA cancer vaccines.

Clinical Pharmacology Applications of Real-World Data and Real-World Evidence in Drug Development and Approval-An Industry Perspective.

Since the 21st Century Cures Act was signed into law in 2016, real-world data (RWD) and real-world evidence (RWE) have attracted great interest from the healthcare ecosystem globally. The potential and capability of RWD/RWE to inform regulatory decisions and clinical drug development have been extensively reviewed and discussed in the literature. However, a comprehensive review of current applications of RWD/RWE in clinical pharmacology, particularly from an industry perspective, is needed to inspire new insights and identify potential future opportunities for clinical pharmacologists to utilize RWD/RWE to address key drug development questions. In this paper, we review the RWD/RWE applications relevant to clinical pharmacology based on recent publications from member companies in the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) RWD Working Group, and discuss the future direction of RWE utilization from a clinical pharmacology perspective. A comprehensive review of RWD/RWE use cases is provided and discussed in the following categories of application: drug-drug interaction assessments, dose recommendation for patients with organ impairment, pediatric plan development and study design, model-informed drug development (e.g., disease progression modeling), prognostic and predictive biomarkers/factors identification, regulatory decisions support (e.g., label expansion), and synthetic/external control generation for rare diseases. Additionally, we describe and discuss common sources of RWD to help guide appropriate data selection to address questions pertaining to clinical pharmacology in drug development and regulatory decision making.

The Effect of Trimethoprim on Thiamine Absorption: A Transporter-Mediated Drug-Nutrient Interaction.

Trimethoprim is predicted to inhibit several thiamine transporters, including the primary thiamine intestinal absorptive transporter, ThTR-2, and the hepatic and renal organic cation transporters, OCT1, OCT2, and MATEs. To investigate the effect of trimethoprim on thiamine absorption, studies were conducted in cells, mice, and healthy volunteers and supported by use of real-world data. In a randomized, crossover clinical study, seven healthy volunteers were given a single oral dose of thiamine or thiamine plus trimethoprim, followed by blood sampling. The thiamine area under the curve (AUC) increased with trimethoprim co-administration (P value = 0.031). Similar results were seen in mice. Trimethoprim appeared to act on thiamine absorption through inhibition of hepatic OCT1 as evidenced from its ability to modulate levels of isobutyrylcarnitine and propionylcarnitine, OCT1 biomarkers identified from metabolomic analyses. Real-world data further supported this finding, showing an association between trimethoprim use and higher levels of triglycerides, LDL cholesterol, and total cholesterol, consistent with OCT1 inhibition (P values: 2.2 × 10-16 , 5.75 × 10-7 , and 5.82 × 10-7 , respectively). These findings suggest that trimethoprim increases plasma levels of thiamine by inhibiting hepatic OCT1. Trimethoprim reduced urinary excretion and clearance of biomarkers for OCT2 and MATEs, consistent with inhibition of renal organic cation transporters. This inhibition did not appear to play a role in the observed increases in thiamine levels. This study highlights the potential for drug-nutrient interactions involving transporters, in addition to transporters' established role in drug-drug interactions.

A New Era in Pharmacovigilance: Toward Real-World Data and Digital Monitoring.

Adverse drug reactions (ADRs) are a major concern for patients, clinicians, and regulatory agencies. The discovery of serious ADRs leading to substantial morbidity and mortality has resulted in mandatory phase IV clinical trials, black box warnings, and withdrawal of drugs from the market. Real-world data, data collected during routine clinical care, is being adopted by innovators, regulators, payors, and providers to inform decision making throughout the product life cycle. We outline several different approaches to modern pharmacovigilance, including spontaneous reporting databases, electronic health record monitoring and research frameworks, social media surveillance, and the use of digital devices. Some of these platforms are well-established while others are still emerging or experimental. We highlight both the potential opportunity, as well as the existing challenges within these pharmacovigilance systems that have already begun to impact the drug development process, as well as the landscape of postmarket drug safety monitoring. Further research and investment into different and complementary pharmacovigilance systems is needed to ensure the continued safety of pharmacotherapy.

Oxypurinol pharmacokinetics and pharmacodynamics in healthy volunteers: Influence of BCRP Q141K polymorphism and patient characteristics.

The missense variant, breast cancer resistance protein (BCRP) p.Q141K, which encodes a reduced function BCRP, has been linked to poor response to allopurinol. Using a multifaceted approach, we aimed to characterize the relationship(s) between BCRP p.Q141K, the pharmacokinetics (PK) and pharmacodynamics (PD) of oxypurinol (the active metabolite of allopurinol), and serum uric acid (SUA) levels. A prospective clinical study (NCT02956278) was conducted in which healthy volunteers were given a single oral dose of 300 mg allopurinol followed by intensive blood sampling. Data were analyzed using noncompartmental analysis and population PK/PD modeling. Additionally, electronic health records were analyzed to investigate whether clinical inhibitors of BCRP phenocopied the effects of the p.Q141K variant with respect to SUA. Subjects homozygous for p.Q141K had a longer half-life (34.2 ± 12.2 h vs. 19.1 ± 1.42 h) of oxypurinol. The PK/PD model showed that women had a 24.8% lower volume of distribution. Baseline SUA was affected by p.Q141K genotype and renal function; that is, it changed by 48.8% for every 1 mg/dl difference in serum creatinine. Real-world data analyses showed that patients prescribed clinical inhibitors of BCRP have higher SUA levels than those that have not been prescribed inhibitors of BCRP, consistent with the idea that BCRP inhibitors phenocopy the effects of p.Q141K on uric acid levels. This study identified important covariates of oxypurinol PK/PD that could affect its efficacy for the treatment of gout as well as a potential side effect of BCRP inhibitors on increasing uric acid levels, which has not been described previously.

The Effects of Genetic Mutations and Drugs on the Activity of the Thiamine Transporter, SLC19A2.

A rare cause of megaloblastic anemia (MA) is thiamine-responsive megaloblastic anemia (TRMA), a genetic disorder caused by mutations in SLC19A2 (encoding THTR1), a thiamine transporter. The study objectives were to (1) functionally characterize selected TRMA-associated SLC19A2 variants and (2) determine whether current prescription drugs associated with drug-induced MA (DIMA) may act via inhibition of SLC19A2. Functional characterization of selected SLC19A2 variants was performed by confocal microscopy and isotopic uptake studies of [3H]-thiamine in HEK293 cells. Sixty-three drugs associated with DIMA were screened for SLC19A2 inhibition in isotopic uptake studies. Three previously uncharacterized SLC19A2 variants identified in TRMA patients exhibited disrupted localization to the plasma membrane along with near-complete loss-of-function. Ten of 63 drugs inhibited SLC19A2-mediated thiamine transport ≥ 50% at screening concentrations; however, with the exception of erythromycin, none was predicted to inhibit SLC19A2 at clinically relevant unbound plasma concentrations. Data from electronic health records revealed reduced levels of thiamine pyrophosphate (TPP) in patients prescribed erythromycin, consistent with inhibition of SLC19A2-mediated thiamine transport. Here, we confirmed the role of three SLC19A2 variants in TRMA pathology. Additionally, we report that inhibition of SLC19A2 is a potential, but uncommon mechanism for DIMA.

Drugs in COVID-19 Clinical Trials: Predicting Transporter-Mediated Drug-Drug Interactions Using In Vitro Assays and Real-World Data.

Numerous drugs are currently under accelerated clinical investigation for the treatment of coronavirus disease 2019 (COVID-19); however, well-established safety and efficacy data for these drugs are limited. The goal of this study was to predict the potential of 25 small molecule drugs in clinical trials for COVID-19 to cause clinically relevant drug-drug interactions (DDIs), which could lead to potential adverse drug reactions (ADRs) with the use of concomitant medications. We focused on 11 transporters, which are targets for DDIs. In vitro potency studies in membrane vesicles or HEK293 cells expressing the transporters coupled with DDI risk assessment methods revealed that 20 of the 25 drugs met the criteria from regulatory authorities to trigger consideration of a DDI clinical trial. Analyses of real-world data from electronic health records, including a database representing nearly 120,000 patients with COVID-19, were consistent with several of the drugs causing transporter-mediated DDIs (e.g., sildenafil, chloroquine, and hydroxychloroquine). This study suggests that patients with COVID-19, who are often older and on various concomitant medications, should be carefully monitored for ADRs. Future clinical studies are needed to determine whether the drugs that are predicted to inhibit transporters at clinically relevant concentrations, actually result in DDIs.

Drug-nutrient interactions: discovering prescription drug inhibitors of the thiamine transporter ThTR-2 (SLC19A3).

BACKGROUND: Transporter-mediated drug-nutrient interactions have the potential to cause serious adverse events. However, unlike drug-drug interactions, these drug-nutrient interactions receive little attention during drug development. The clinical importance of drug-nutrient interactions was highlighted when a phase III clinical trial was terminated due to severe adverse events resulting from potent inhibition of thiamine transporter 2 (ThTR-2; SLC19A3). OBJECTIVE: In this study, we tested the hypothesis that therapeutic drugs inhibit the intestinal thiamine transporter ThTR-2, which may lead to thiamine deficiency. METHODS: For this exploration, we took a multifaceted approach, starting with a high-throughput in vitro primary screen to identify inhibitors, building in silico models to characterize inhibitors, and leveraging real-world data from electronic health records to begin to understand the clinical relevance of these inhibitors. RESULTS: Our high-throughput screen of 1360 compounds, including many clinically used drugs, identified 146 potential inhibitors at 200 µM. Inhibition kinetics were determined for 28 drugs with half-maximal inhibitory concentration (IC50) values ranging from 1.03 µM to >1 mM. Several oral drugs, including metformin, were predicted to have intestinal concentrations that may result in ThTR-2-mediated drug-nutrient interactions. Complementary analysis using electronic health records suggested that thiamine laboratory values are reduced in individuals receiving prescription drugs found to significantly inhibit ThTR-2, particularly in vulnerable populations (e.g., individuals with alcoholism). CONCLUSIONS: Our comprehensive analysis of prescription drugs suggests that several marketed drugs inhibit ThTR-2, which may contribute to thiamine deficiency, especially in at-risk populations.

Development of a label-free FcRn-mediated transcytosis assay for in vitro characterization of FcRn interactions with therapeutic antibodies and Fc-fusion proteins.

The neonatal Fc receptor (FcRn) binds to the Fc domain of IgG in a pH-dependent manner, guides the intracellular movement of the bound antibodies and protects them from lysosomal degradation. Proper characterization of Fc-FcRn interactions is fundamental to successful design, development, and production of Fc-containing therapeutic proteins because of the potential impact of such interactions on their in vivo pharmacokinetic behaviors. Here, we describe the development and characterization of a cell-based, label-free FcRn-mediated transcytosis assay that provides a functional readout to reflect the totality of Fc-FcRn interactions, including pH-dependent association and dissociation, as well as the intracellular trafficking of Fc-containing molecules in complex with FcRn. Our study demonstrates that this transcytosis assay can be used to evaluate FcRn binding of therapeutic antibodies and Fc-fusion proteins, including wild-type and engineered Fc variants with varying FcRn binding affinities, as well as oxidized and aggregated antibody samples. These results support the utility of an FcRn-dependent transcytosis assay for evaluation of both Fc-FcRn interactions and the structural integrity of Fc-containing therapeutic proteins pertinent to their pharmacokinetic behavior in vivo.

Meta-Analysis of Maternal and Fetal Transcriptomic Data Elucidates the Role of Adaptive and Innate Immunity in Preterm Birth.

Preterm birth (PTB) is the leading cause of newborn deaths around the world. Spontaneous preterm birth (sPTB) accounts for two-thirds of all PTBs; however, there remains an unmet need of detecting and preventing sPTB. Although the dysregulation of the immune system has been implicated in various studies, small sizes and irreproducibility of results have limited identification of its role. Here, we present a cross-study meta-analysis to evaluate genome-wide differential gene expression signals in sPTB. A comprehensive search of the NIH genomic database for studies related to sPTB with maternal whole blood samples resulted in data from three separate studies consisting of 339 samples. After aggregating and normalizing these transcriptomic datasets and performing a meta-analysis, we identified 210 genes that were differentially expressed in sPTB relative to term birth. These genes were enriched in immune-related pathways, showing upregulation of innate immunity and downregulation of adaptive immunity in women who delivered preterm. An additional analysis found several of these differentially expressed at mid-gestation, suggesting their potential to be clinically relevant biomarkers. Furthermore, a complementary analysis identified 473 genes differentially expressed in preterm cord blood samples. However, these genes demonstrated downregulation of the innate immune system, a stark contrast to findings using maternal blood samples. These immune-related findings were further confirmed by cell deconvolution as well as upstream transcription and cytokine regulation analyses. Overall, this study identified a strong immune signature related to sPTB as well as several potential biomarkers that could be translated to clinical use.