Assessing the effect of selective serotonin reuptake inhibitors in the prevention of post-acute sequelae of COVID-19.

Background: Post-acute sequelae of COVID-19 (PASC) produce significant morbidity, prompting evaluation of interventions that might lower risk. Selective serotonin reuptake inhibitors (SSRIs) potentially could modulate risk of PASC via their central, hypothesized immunomodulatory, and/or antiplatelet properties although clinical trial data are lacking. Materials and Methods: This retrospective study was conducted leveraging real-world clinical data within the National COVID Cohort Collaborative (N3C) to evaluate whether SSRIs with agonist activity at the sigma-1 receptor (S1R) lower the risk of PASC, since agonism at this receptor may serve as a mechanism by which SSRIs attenuate an inflammatory response. Additionally, determine whether the potential benefit could be traced to S1R agonism. Presumed PASC was defined based on a computable PASC phenotype trained on the U09.9 ICD-10 diagnosis code. Results: Of the 17,908 patients identified, 1521 were exposed at baseline to a S1R agonist SSRI, 1803 to a non-S1R agonist SSRI, and 14,584 to neither. Using inverse probability weighting and Poisson regression, relative risk (RR) of PASC was assessed.A 29% reduction in the RR of PASC (0.704 [95% CI, 0.58-0.85]; P = 4 ×10-4) was seen among patients who received an S1R agonist SSRI compared to SSRI unexposed patients and a 21% reduction in the RR of PASC was seen among those receiving an SSRI without S1R agonist activity (0.79 [95% CI, 0.67 - 0.93]; P = 0.005).Thus, SSRIs with and without reported agonist activity at the S1R were associated with a significant decrease in the risk of PASC.

Privacy-preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience.

Introduction: Research driven by real-world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID-19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants. The objective of this project was to establish privacy-preserving record linkage (PPRL) methods to ensure that patient-level EHR data remains secure and private when governance-approved linkages with other datasets occur. Methods: Separate agreements and approval processes govern N3C data contribution and data access. The Linkage Honest Broker (LHB), an independent neutral party (the Regenstrief Institute), ensures data linkages are robust and secure by adding an extra layer of separation between protected health information and clinical data. The LHB's PPRL methods (including algorithms, processes, and governance) match patient records using "deidentified tokens," which are hashed combinations of identifier fields that define a match across data repositories without using patients' clear-text identifiers. Results: These methods enable three linkage functions: Deduplication, Linking Multiple Datasets, and Cohort Discovery. To date, two external repositories have been cross-linked. As of March 1, 2023, 43 sites have signed the LHB Agreement; 35 sites have sent tokens generated for 9 528 998 patients. In this initial cohort, the LHB identified 135 037 matches and 68 596 duplicates. Conclusion: This large-scale linkage study using deidentified datasets of varying characteristics established secure methods for protecting the privacy of N3C patient data when linked for research purposes. This technology has potential for use with registries for other diseases and conditions.

High-Throughput Screening to Advance In Vitro Toxicology: Accomplishments, Challenges, and Future Directions.

Traditionally, chemical toxicity is determined by in vivo animal studies, which are low throughput, expensive, and sometimes fail to predict compound toxicity in humans. Due to the increasing number of chemicals in use and the high rate of drug candidate failure due to toxicity, it is imperative to develop in vitro, high-throughput screening methods to determine toxicity. The Tox21 program, a unique research consortium of federal public health agencies, was established to address and identify toxicity concerns in a high-throughput, concentration-responsive manner using a battery of in vitro assays. In this article, we review the advancements in high-throughput robotic screening methodology and informatics processes to enable the generation of toxicological data, and their impact on the field; further, we discuss the future of assessing environmental toxicity utilizing efficient and scalable methods that better represent the corresponding biological and toxicodynamic processes in humans.

Automated measurement of transepithelial electrical resistance (TEER) in 96-well transwells using ECIS TEER96: Single and multiple time point assessments.

Transepithelial electrical resistance (TEER) is a widely used technique for quantifying the permeability of epithelial and endothelial cell layers. However, traditional methods of measuring TEER are limited to single timepoint measurements and can subject cells to an altered environment during the measurement. Here, we assessed the validity of TEER measurements by the ECIS TEER96 device, which is designed to take continuous TEER measurements of a cell culture system in a standard laboratory incubator. We found that the instrument accurately measures TEER across TEER values ranging from 10 to 2050 Ω*cm2 and is more accurate than the manual epithelial voltohmmeter electrode at high TEER values. Furthermore, the high-resolution measurements provided by the device allowed for a unique insight into the mechanisms and kinetics of cells in vitro. To demonstrate the continuous measurement capability of the device, we tracked the formation of an MDCKI cell monolayer until TEER plateaued. Furthermore, we treated Caco-2 monolayers with different concentrations of DMSO and the antimicrobial and surfactant compound benzethonium chloride to measure disruptions to barrier integrity. Treatment of both compounds resulted in concentration-dependent loss of barrier integrity. Our results suggest that the ECIS TEER96 device is a reliable and convenient option for measuring TEER in cell cultures and can provide valuable insights into the behavior of cells in vitro. This technology will be especially useful for increasing throughput of drug permeability assays, inflammation studies, and gaining better understanding of disease states in a cell culture system.

A defined roadmap of radial glia and astrocyte differentiation from human pluripotent stem cells.

Human gliogenesis remains poorly understood, and derivation of astrocytes from human pluripotent stem cells (hPSCs) is inefficient and cumbersome. Here, we report controlled glial differentiation from hPSCs that bypasses neurogenesis, which otherwise precedes astrogliogenesis during brain development and in vitro differentiation. hPSCs were first differentiated into radial glial cells (RGCs) resembling resident RGCs of the fetal telencephalon, and modulation of specific cell signaling pathways resulted in direct and stepwise induction of key astroglial markers (NFIA, NFIB, SOX9, CD44, S100B, glial fibrillary acidic protein [GFAP]). Transcriptomic and genome-wide epigenetic mapping and single-cell analysis confirmed RGC-to-astrocyte differentiation, obviating neurogenesis and the gliogenic switch. Detailed molecular and cellular characterization experiments uncovered new mechanisms and markers for human RGCs and astrocytes. In summary, establishment of a glia-exclusive neural lineage progression model serves as a unique serum-free platform of manufacturing large numbers of RGCs and astrocytes for neuroscience, disease modeling (e.g., Alexander disease), and regenerative medicine.

A landscape of response to drug combinations in non-small cell lung cancer.

Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.

Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C).

BACKGROUND: Multi-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical interventions. They provide access to clinical data from large pooled patient populations in addition to laboratory measurements unavailable in insurance claims-based data. However, secondary use of these data for research requires specialized knowledge and careful evaluation of data quality and completeness. We discuss data quality assessments undertaken during the conduct of prep-to-research, focusing on the investigation of treatment safety and effectiveness. METHODS: Using the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria typical in non-interventional inpatient drug effectiveness studies. We present the challenges encountered when constructing this dataset, beginning with an examination of data quality across data partners. We then discuss the methods and best practices used to operationalize several important study elements: exposure to treatment, baseline health comorbidities, and key outcomes of interest. RESULTS: We share our experiences and lessons learned when working with heterogeneous EHR data from over 65 healthcare institutions and 4 common data models. We discuss six key areas of data variability and quality. (1) The specific EHR data elements captured from a site can vary depending on source data model and practice. (2) Data missingness remains a significant issue. (3) Drug exposures can be recorded at different levels and may not contain route of administration or dosage information. (4) Reconstruction of continuous drug exposure intervals may not always be possible. (5) EHR discontinuity is a major concern for capturing history of prior treatment and comorbidities. Lastly, (6) access to EHR data alone limits the potential outcomes which can be used in studies. CONCLUSIONS: The creation of large scale centralized multi-site EHR databases such as N3C enables a wide range of research aimed at better understanding treatments and health impacts of many conditions including COVID-19. As with all observational research, it is important that research teams engage with appropriate domain experts to understand the data in order to define research questions that are both clinically important and feasible to address using these real world data.

Assessing the Effect of Selective Serotonin Reuptake Inhibitors in the Prevention of Post-Acute Sequelae of COVID-19.

Importance: Post-acute sequelae of COVID-19 (PASC) produce significant morbidity, prompting evaluation of interventions that might lower risk. Selective serotonin reuptake inhibitors (SSRIs) potentially could modulate risk of PASC via their central, hypothesized immunomodulatory, and/or antiplatelet properties and therefore may be postulated to be of benefit in patients with PASC, although clinical trial data are lacking. Objectives: The main objective was to evaluate whether SSRIs with agonist activity at the sigma-1 receptor lower the risk of PASC, since agonism at this receptor may serve as a mechanism by which SSRIs attenuate an inflammatory response. A secondary objective was to determine whether potential benefit could be traced to sigma-1 agonism by evaluating the risk of PASC among recipients of SSRIs that are not S1R agonists. Design: Retrospective study leveraging real-world clinical data within the National COVID Cohort Collaborative (N3C), a large centralized multi-institutional de-identified EHR database. Presumed PASC was defined based on a computable PASC phenotype trained on the U09.9 ICD-10 diagnosis code to more comprehensively identify patients likely to have the condition, since the ICD code has come into wide-spread use only recently. Setting: Population-based study at US medical centers. Participants: Adults (≥ 18 years of age) with a confirmed COVID-19 diagnosis date between October 1, 2021 and April 7, 2022 and at least one follow up visit 45 days post-diagnosis. Of the 17 933 patients identified, 2021 were exposed at baseline to a S1R agonist SSRI, 1328 to a non-S1R agonist SSRI, and 14 584 to neither. Exposures: Exposure at baseline (at or prior to COVID-19 diagnosis) to an SSRI with documented or presumed agonist activity at the S1R (fluvoxamine, fluoxetine, escitalopram, or citalopram), an SSRI without agonist activity at S1R (sertraline, an antagonist, or paroxetine, which does not appreciably bind to the S1R), or none of these agents. Main Outcome and Measurement: Development of PASC based on a previously validated XGBoost-trained algorithm. Using inverse probability weighting and Poisson regression, relative risk (RR) of PASC was assessed. Results: A 26% reduction in the RR of PASC (0.74 [95% CI, 0.63-0.88]; P = 5 × 10-4) was seen among patients who received an S1R agonist SSRI compared to SSRI unexposed patients and a 25% reduction in the RR of PASC was seen among those receiving an SSRI without S1R agonist activity (0.75 [95% CI, 0.62 - 0.90]; P = 0.003) compared to SSRI unexposed patients. Conclusions and Relevance: SSRIs with and without reported agonist activity at the S1R were associated with a significant decrease in the risk of PASC. Future prospective studies are warranted.

Bioprinted 3D outer retina barrier uncovers RPE-dependent choroidal phenotype in advanced macular degeneration.

Age-related macular degeneration (AMD), a leading cause of blindness, initiates in the outer-blood-retina-barrier (oBRB) formed by the retinal pigment epithelium (RPE), Bruch's membrane, and choriocapillaris. The mechanisms of AMD initiation and progression remain poorly understood owing to the lack of physiologically relevant human oBRB models. To this end, we engineered a native-like three-dimensional (3D) oBRB tissue (3D-oBRB) by bioprinting endothelial cells, pericytes, and fibroblasts on the basal side of a biodegradable scaffold and establishing an RPE monolayer on top. In this 3D-oBRB model, a fully-polarized RPE monolayer provides barrier resistance, induces choriocapillaris fenestration, and supports the formation of Bruch's-membrane-like structure by inducing changes in gene expression in cells of the choroid. Complement activation in the 3D-oBRB triggers dry AMD phenotypes (including subRPE lipid-rich deposits called drusen and choriocapillaris degeneration), and HIF-α stabilization or STAT3 overactivation induce choriocapillaris neovascularization and type-I wet AMD phenotype. The 3D-oBRB provides a physiologically relevant model to studying RPE-choriocapillaris interactions under healthy and diseased conditions.

New-onset Bell's palsy after neuroinvasive West Nile virus.

In this case report, a patient was diagnosed with new-onset Bell's palsy 3 weeks after the onset of neuroinvasive West Nile virus. This was the second case report of West Nile virus-associated Bell's palsy, highlighting the need to monitor these patients for peripheral neuropathies. This case report is also intended to raise awareness about the prevalence of West Nile virus in the USA.

Retro Drug Design: From Target Properties to Molecular Structures.

To deliver more therapeutics to more patients more quickly and economically is the ultimate goal of pharmaceutical researchers. The advent and rapid development of artificial intelligence (AI), in combination with other powerful computational methods in drug discovery, makes this goal more practical than ever before. Here, we describe a new strategy, retro drug design, or RDD, to create novel small-molecule drugs from scratch to meet multiple predefined requirements, including biological activity against a drug target and optimal range of physicochemical and ADMET properties. The molecular structure was represented by an atom typing based molecular descriptor system, optATP, which was further transformed to the space of loading vectors from principal component analysis. Traditional predictive models were trained over experimental data for the target properties using optATP and shallow machine learning methods. The Monte Carlo sampling algorithm was then utilized to find the solutions in the space of loading vectors that have the target properties. Finally, a deep learning model was employed to decode molecular structures from the solutions. To test the feasibility of the algorithm, we challenged RDD to generate novel kinase inhibitors from random numbers with five different ADMET properties optimized at the same time. The best Tanimoto similarity score between the generated valid structures and the available 4,314 kinase inhibitors was < 0.50, indicating a high extent of novelty of the generated compounds. From the 3,040 structures that met all six target properties, 20 were selected for synthesis and experimental measurement of inhibition activity over 97 representative kinases and the ADMET properties. Fifteen and eight compounds were determined to be hits or strong hits, respectively. Five of the six strong kinase inhibitors have excellent experimental ADMET properties. The results presented in this paper illustrate that RDD has the potential to significantly improve the current drug discovery process.

High-Throughput Cellular Thermal Shift Assay Using Acoustic Transfer of Protein Lysates.

Cellular thermal shift assay (CETSA) is a valuable method to confirm target engagement within a complex cellular environment, by detecting changes in a protein's thermal stability upon ligand binding. The classical CETSA method measures changes in the thermal stability of endogenous proteins using immunoblotting, which is low-throughput and laborious. Reverse-phase protein arrays (RPPAs) have been demonstrated as a detection modality for CETSA; however, the reported procedure requires manual processing steps that limit throughput and preclude screening applications. We developed a high-throughput CETSA using an acoustic RPPA (HT-CETSA-aRPPA) protocol that is compatible with 96- and 384-well microplates from start-to-finish, using low speed centrifugation to remove thermally destabilized proteins. The utility of HT-CETSA-aRPPA for guiding structure-activity relationship studies was demonstrated for inhibitors of lactate dehydrogenase A. Additionally, a collection of kinase inhibitors was screened to identify compounds that engage MEK1, a clinically relevant kinase target.

Correction to: Human Pluripotent Stem Cells for High-Throughput Drug Screening and Characterization of Small Molecules.

This chapter was inadvertently published with an error, the ninth point under 'Materials' section must read.

A high throughput screening assay for inhibitors of SARS-CoV-2 pseudotyped particle entry.

Effective small molecule therapies to combat the SARS-CoV-2 infection are still lacking as the COVID-19 pandemic continues globally. High throughput screening assays are needed for lead discovery and optimization of small molecule SARS-CoV-2 inhibitors. In this work, we have applied viral pseudotyping to establish a cell-based SARS-CoV-2 entry assay. Here, the pseudotyped particles (PP) contain SARS-CoV-2 spike in a membrane enveloping both the murine leukemia virus (MLV) gag-pol polyprotein and luciferase reporter RNA. Upon addition of PP to HEK293-ACE2 cells, the SARS-CoV-2 spike protein binds to the ACE2 receptor on the cell surface, resulting in priming by host proteases to trigger endocytosis of these particles, and membrane fusion between the particle envelope and the cell membrane. The internalized luciferase reporter gene is then expressed in cells, resulting in a luminescent readout as a surrogate for spike-mediated entry into cells. This SARS-CoV-2 PP entry assay can be executed in a biosafety level 2 containment lab for high throughput screening. From a collection of 5,158 approved drugs and drug candidates, our screening efforts identified 7 active compounds that inhibited the SARS-CoV-2-S PP entry. Of these seven, six compounds were active against live replicating SARS-CoV-2 virus in a cytopathic effect assay. Our results demonstrated the utility of this assay in the discovery and development of SARS-CoV-2 entry inhibitors as well as the mechanistic study of anti-SARS-CoV-2 compounds. Additionally, particles pseudotyped with spike proteins from SARS-CoV-2 B.1.1.7 and B.1.351 variants were prepared and used to evaluate the therapeutic effects of viral entry inhibitors.

Human Pluripotent Stem Cells for High-Throughput Drug Screening and Characterization of Small Molecules.

Human pluripotent stem cells (hPSCs), such as induced pluripotent stem cells (iPSCs), hold great promise for drug discovery, toxicology studies, and regenerative medicine. Here, we describe standardized protocols and experimental procedures that combine automated cell culture for scalable production of hPSCs with quantitative high-throughput screening (qHTS) in miniaturized 384-well plates. As a proof of principle, we established dose-response assessments and determined optimal concentrations of 12 small molecule compounds that are commonly used in the stem cell field. Multi-parametric analysis of readouts from diverse assays including cell viability, mitochondrial membrane potential, plasma membrane integrity, and ATP production was used to distinguish normal biological responses from cellular stress induced by small molecule treatment. Collectively, the establishment of integrated workflows for cell manufacturing, qHTS, high-content imaging, and data analysis provides an end-to-end platform for industrial-scale projects and should leverage the drug discovery process using hPSC-derived cell types.

Robotic high-throughput biomanufacturing and functional differentiation of human pluripotent stem cells.

Efficient translation of human induced pluripotent stem cells (hiPSCs) requires scalable cell manufacturing strategies for optimal self-renewal and functional differentiation. Traditional manual cell culture is variable and labor intensive, posing challenges for high-throughput applications. Here, we established a robotic platform and automated all essential steps of hiPSC culture and differentiation under chemically defined conditions. This approach allowed rapid and standardized manufacturing of billions of hiPSCs that can be produced in parallel from up to 90 different patient- and disease-specific cell lines. Moreover, we established automated multi-lineage differentiation and generated functional neurons, cardiomyocytes, and hepatocytes. To validate our approach, we compared robotic and manual cell culture operations and performed comprehensive molecular and cellular characterizations (e.g., single-cell transcriptomics, mass cytometry, metabolism, electrophysiology) to benchmark industrial-scale cell culture operations toward building an integrated platform for efficient cell manufacturing for disease modeling, drug screening, and cell therapy.

Cross-Platform Bayesian Optimization System for Autonomous Biological Assay Development.

Current high-throughput screening assay optimization is often a manual and time-consuming process, even when utilizing design-of-experiment approaches. A cross-platform, Cloud-based Bayesian optimization-based algorithm was developed as part of the National Center for Advancing Translational Sciences (NCATS) ASPIRE (A Specialized Platform for Innovative Research Exploration) Initiative to accelerate preclinical drug discovery. A cell-free assay for papain enzymatic activity was used as proof of concept for biological assay development and system operationalization. Compared with a brute-force approach that sequentially tested all 294 assay conditions to find the global optimum, the Bayesian optimization algorithm could find suitable conditions for optimal assay performance by testing 21 assay conditions on average, with up to 20 conditions being tested simultaneously, as confirmed by repeated simulation. The algorithm could achieve a sevenfold reduction in costs for lab supplies and high-throughput experimentation runtime, all while being controlled from a remote site through a secure connection. Based on this proof of concept, this technology is expected to be applied to more complex biological assays and automated chemistry reaction screening at NCATS, and should be transferable to other institutions.

A high throughput screening assay for inhibitors of SARS-CoV-2 pseudotyped particle entry.

Effective small molecule therapies to combat the SARS-CoV-2 infection are still lacking as the COVID-19 pandemic continues globally. High throughput screening assays are needed for lead discovery and optimization of small molecule SARS-CoV-2 inhibitors. In this work, we have applied viral pseudotyping to establish a cell-based SARS-CoV-2 entry assay. Here, the pseudotyped particles (PP) contain SARS-CoV-2 spike in a membrane enveloping both the murine leukemia virus (MLV) gag-pol polyprotein and luciferase reporter RNA. Upon addition of PP to HEK293-ACE2 cells, the SARS-CoV-2 spike protein binds to the ACE2 receptor on the cell surface, resulting in priming by host proteases to trigger endocytosis of these particles, and membrane fusion between the particle envelope and the cell membrane. The internalized luciferase reporter gene is then expressed in cells, resulting in a luminescent readout as a surrogate for spike-mediated entry into cells. This SARS-CoV-2 PP entry assay can be executed in a biosafety level 2 containment lab for high throughput screening. From a collection of 5,158 approved drugs and drug candidates, our screening efforts identified 7 active compounds that inhibited the SARS-CoV-2-S PP entry. Of these seven, six compounds were active against live replicating SARS-CoV-2 virus in a cytopathic effect assay. Our results demonstrated the utility of this assay in the discovery and development of SARS-CoV-2 entry inhibitors as well as the mechanistic study of anti-SARS-CoV-2 compounds. Additionally, particles pseudotyped with spike proteins from SARS-CoV-2 B.1.1.7 and B.1.351 variants were prepared and used to evaluate the therapeutic effects of viral entry inhibitors.

Undiagnosed SARS-CoV-2 seropositivity during the first 6 months of the COVID-19 pandemic in the United States.

Asymptomatic SARS-CoV-2 infection and delayed implementation of diagnostics have led to poorly defined viral prevalence rates in the United States and elsewhere. To address this, we analyzed seropositivity in 9089 adults in the United States who had not been diagnosed previously with COVID-19. Individuals with characteristics that reflected the U.S. population (n = 27,716) were selected by quota sampling from 462,949 volunteers. Enrolled participants (n = 11,382) provided medical, geographic, demographic, and socioeconomic information and dried blood samples. Survey questions coincident with the Behavioral Risk Factor Surveillance System survey, a large probability-based national survey, were used to adjust for selection bias. Most blood samples (88.7%) were collected between 10 May and 31 July 2020 and were processed using ELISA to measure seropositivity (IgG and IgM antibodies against SARS-CoV-2 spike protein and the spike protein receptor binding domain). The overall weighted undiagnosed seropositivity estimate was 4.6% (95% CI, 2.6 to 6.5%), with race, age, sex, ethnicity, and urban/rural subgroup estimates ranging from 1.1% to 14.2%. The highest seropositivity estimates were in African American participants; younger, female, and Hispanic participants; and residents of urban centers. These data indicate that there were 4.8 undiagnosed SARS-CoV-2 infections for every diagnosed case of COVID-19, and an estimated 16.8 million infections were undiagnosed by mid-July 2020 in the United States.

Retro Drug Design: From Target Properties to Molecular Structures.

To generate drug molecules of desired properties with computational methods is the holy grail in pharmaceutical research. Here we describe an AI strategy, retro drug design, or RDD, to generate novel small molecule drugs from scratch to meet predefined requirements, including but not limited to biological activity against a drug target, and optimal range of physicochemical and ADMET properties. Traditional predictive models were first trained over experimental data for the target properties, using an atom typing based molecular descriptor system, ATP. Monte Carlo sampling algorithm was then utilized to find the solutions in the ATP space defined by the target properties, and the deep learning model of Seq2Seq was employed to decode molecular structures from the solutions. To test feasibility of the algorithm, we challenged RDD to generate novel drugs that can activate µ opioid receptor (MOR) and penetrate blood brain barrier (BBB). Starting from vectors of random numbers, RDD generated 180,000 chemical structures, of which 78% were chemically valid. About 42,000 (31%) of the valid structures fell into the property space defined by MOR activity and BBB permeability. Out of the 42,000 structures, only 267 chemicals were commercially available, indicating a high extent of novelty of the AI-generated compounds. We purchased and assayed 96 compounds, and 25 of which were found to be MOR agonists. These compounds also have excellent BBB scores. The results presented in this paper illustrate that RDD has potential to revolutionize the current drug discovery process and create novel structures with multiple desired properties, including biological functions and ADMET properties. Availability of an AI-enabled fast track in drug discovery is essential to cope with emergent public health threat, such as pandemic of COVID-19.