Tool For Drug Discovery Research Articles

The study of regression model based on CoM-polynomial in blood cancer drug properties

A topological index (TI) is a real number that determined the structure of a graph. Topological indices (TIs) serves mathematical descriptors that aid in predicting and understanding the physicochemical properties and structure-activity relationships of chemical compounds, thus facilitating drug design and discovery processes. TIs utilizes to forecast the effectiveness of cancer treatment by providing details on the molecules of the medications and their associated properties. In order to create safe and effective treatments, it is crucial to employ TIs to accurately predict the toxicity and side effects of medications. In this paper, we have considered the physical and biological properties of some blood cancer treatment medications through degree based Banhatti topological indices from the M-polynomial by using derivations. The drugs are Palbociclib, Ivosidenib, Glasdegib, Gilteritinib, and Daunorubicin and the TI's are given in Table 1. Subsequently, a linear regression technique is employed to establish a quantitative structure-property relationship (QSPR) model for predicting various parameters such as molecular weight, complexity, molar volume, boiling point (BP), and flash point (FP). Results indicate that topological indices have potential as a drug discovery and development tool in the fight against cancer.

PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning.

Many machine learning techniques are used as drug discovery tools with the intent to speed characterization by determining relationships between compound structure and biological function. However, particularly in anticancer drug discovery, these models often make only binary decisions about the biological activity for a narrow scope of drug targets. We present a feed-forward neural network, PECAN (Prediction Engine for the Cytostatic Activity of Natural product-like compounds), that simultaneously classifies the potential antiproliferative activity of compounds against 59 cancer cell lines. It predicts the activity to be one of six categories, indicating not only if activity is present but the degree of activity. Using an independent subset of NCI data as a test set, we show that PECAN can reach 60.1% accuracy in a six-way classification and present further evidence that it classifies based on useful structural features of compounds using a "within-one" measure that reaches 93.0% accuracy.

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery.

Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.

Bioinformatics – Supporting modern life science research, applications, and challenges

Bioinformatics is an interdisciplinary field that develops methods, software tools for understanding biological data and aims to investigate questions about biological composition, structure, function, and evolution of molecules, cells, tissues, and organisms using mathematics, informatics, statistics, and computer science. As we are moving towards the era of cutting-edge technologies there will be a lot of data to store, process and analyze. It offers analysis software for data studies and comparisons and provides tools for modeling, visualizing, exploring and interpreting data. It includes analysis, structural and functional characterization of biomolecules leading to the development of Genomics, Proteomics, Transcriptomics, and Metabolomics, etc. Drug discovery and development tools, supported by recent advancements in machine learning and cloud computing should shorten the time to find and produce an efficient drug compound with fewer side effects and more results emerge as a branch called Chemo-informatics. Personalized medicine where bioinformatics can help a lot to make drug molecules based on the genetic makeup of individuals for better outcomes is a prime area of research and need of the society at present. The major futures challenge of the scientific community is to create an in-vitro model of whole-cell or organism and further simulating a whole cell or an organism by applying in-silico approaches. To achieve that, reliable tools that utilize those technologies need to be developed and tested. Bioinformatics reduces the search space/size of the problem by thousand times. The main goal is to convert a multitude of complex data into useful information and knowledge. As a consequence of understanding such data, one can basically engineer longer life for society.

Integrating Artificial Intelligence and PET Imaging for Drug Discovery: A Paradigm Shift in Immunotherapy.

The integration of artificial intelligence (AI) and positron emission tomography (PET) imaging has the potential to become a powerful tool in drug discovery. This review aims to provide an overview of the current state of research and highlight the potential for this alliance to advance pharmaceutical innovation by accelerating the development and deployment of novel therapeutics. We previously performed a scoping review of three databases (Embase, MEDLINE, and CENTRAL), identifying 87 studies published between 2018 and 2022 relevant to medical imaging (e.g., CT, PET, MRI), immunotherapy, artificial intelligence, and radiomics. Herein, we reexamine the previously identified studies, performing a subgroup analysis on articles specifically utilizing AI and PET imaging for drug discovery purposes in immunotherapy-treated oncology patients. Of the 87 original studies identified, 15 met our updated search criteria. In these studies, radiomics features were primarily extracted from PET/CT images in combination (n = 9, 60.0%) rather than PET imaging alone (n = 6, 40.0%), and patient cohorts were mostly recruited retrospectively and from single institutions (n = 10, 66.7%). AI models were used primarily for prognostication (n = 6, 40.0%) or for assisting in tumor phenotyping (n = 4, 26.7%). About half of the studies stress-tested their models using validation sets (n = 4, 26.7%) or both validation sets and test sets (n = 4, 26.7%), while the remaining six studies (40.0%) either performed no validation at all or used less stringent methods such as cross-validation on the training set. Overall, the integration of AI and PET imaging represents a paradigm shift in drug discovery, offering new avenues for more efficient development of therapeutics. By leveraging AI algorithms and PET imaging analysis, researchers could gain deeper insights into disease mechanisms, identify new drug targets, or optimize treatment regimens. However, further research is needed to validate these findings and address challenges such as data standardization and algorithm robustness.

Abstract A060: Organoid Culture as a Unique Resource for Studying Aggressive Triple Negative Breast Cancer in African American

Abstract Introduction Triple negative breast cancer (TNBC) among women with African ancestry has been shown to be more aggressive than in women of European ancestry. However, the root cause for this disparity is not fully understood. In our previous studies, we show that the gene expression in the breast cancer stem cells of TNBC tumors was differential based on Western sub-Saharan ancestry. To study the role of African ancestry in the biology of TNBC, we have developed patient-derived organoids (PDO) from African (Ghanaian) and African American tumors that will be available for research. Organoid culture has the capacity to mimic the physiological tissue organization and functionality and has been used as an invaluable tool for disease modeling and drug discovery. Objective The objective is to develop and characterize PDOs based on African TNBC that will serve as a renewable source of African tumors for breast cancer research. Methods Through our international collaboration called Precision Medicine for Aggressive Breast Cancers (PMABC), we obtained fresh patient tumors from continental Africa (Ghana) and African American tumors from Henry Ford Health. PDOs were developed form both primary tumors and patient-derived xenografts (PDX). Tissues were processed using the Miltenyi Gentle Mac. The cells were seeded in a 12-well plate coated with Matrigel. Developed organoids are characterized using single cell RNA sequencing as well as 10X spatial sequencing Results We have successfully developed organoids using frozen tissues from both West African and African American breast patients. We have successfully established four (4) organoid lines from individual primary breast cancer tissues and one PDX. The organoids are passaged after every 7-14 days, and the highest passage so far is passage number 12 for the organoids from primary tissues and passage number 15 for the PDXs. We are in the process of characterizing the developed organoids using single-cell assay in comparisons to their primary tumor of origin. Conclusion Establishment of organoids has helped us have a renewable source of tumor for studying the aggressive TNBC among Women with African ancestry. We aim to use the organoids to identify tumor biomarkers for treating aggressive TNBC that disproportionately affects women of western sub Saharan ancestry. Citation Format: Moses Kamita, Kurt Fernando, Sylvester Antwi, Forster Amponsah-Manu, Josephine Nsaful, Kafui Akakpo, Jessica Bensenhaver, Kwabena Agbedinu, Michael Nortey, Nelson Affram, Mohammed Sheriff, Cassandra Mills, Alex Mremi, Jieoma Aja, Moses Dokurugu, Haythem Ali, Eleanor Walker, Max Wicha, Evelyn Jiagge. Organoid Culture as a Unique Resource for Studying Aggressive Triple Negative Breast Cancer in African American [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A060.

P154 Histological disease progression and ALK5i therapeutic efficacy in a chronic DSS-induced mouse model of IBD with intestinal fibrosis

Abstract Background Inflammatory bowel disease (IBD) comprises a group of intestinal disorders, including ulcerative colitis and Crohn's disease. Intestinal fibrosis, as a result of chronic inflammation, is a common complication in IBD. Many patients do not respond or do not have a sustained response to existing treatments, highlighting the large unmet need for more effective drugs in the management of IBD. Translational animal models demonstrating chronic, progressive colonic fibrosis are important tools in preclinical drug discovery for IBD. The aim of the present study was to characterize histological disease progression and therapeutic efficacy of a TGF-β type I receptor inhibitor (ALK5i) in a novel chronic DSS-induced mouse model of IBD. Methods 10 week old male C57BL/6JRj mice received 3 cycles of DSS (2% w/v) administration in the drinking water (DSS-IBD) or normal water (CTRL). Study groups were terminated at week 1, 3 or 6. Groups terminated at week 6 received twice daily oral dosing with vehicle or ALK5i (30 mg/kg, BID). Terminal endpoints included distal colon morphometry, quantitative histological markers of inflammation and fibrosis as well as colon transcriptomics. Results Compared to healthy controls, the chronic DSS-IBD mouse model demonstrated clinical and histological hallmarks of progressive IBD, including mild-to-moderate weight loss, colonic hypertrophy with sustained inflammation and fibrosis in the mucosa and submucosa areas. ALK5i treatment increased colon weight/length ratio while significantly reducing fractional (%) area of alpha-1 type I collagen in the colonic mucosa and submucosa. The histopathological phenotype was supported by corresponding regulations in gene expression markers of colonic inflammation including ptprc (cd45), tnf and nos2. Alk5i therapy did not influence gene expression markers of fibrosis, suggesting that improved relative (%) levels of collagen were driven by Alk5i-stimulated tissue volume increases. Further studies are needed to establish if TGF-beta inhibitors have antifibrotic effects in the chronic DSS model of IBD complicated by fibrosis. Conclusion The DSS-IBD mouse model is a preclinical model with features of progressive IBD suitable for testing novel anti-fibrotic drug therapies targeted for IBD patients.

A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel.

The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning.

AI-organoid integrated systems for biomedical studies and applications.

In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid-based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label-free organoid recognition, and three-dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI-organoid integration, focusing on the establishment of reliable AI model decision-making processes and the standardization of organoid research.

Identification of a Novel Subtype-Selective α1B-Adrenoceptor Antagonist.

α1A-, α1B-, and α1D-adrenoceptors (α1-ARs) are members of the adrenoceptor G protein-coupled receptor family that are activated by adrenaline (epinephrine) and noradrenaline. α1-ARs are clinically targeted using antagonists that have minimal subtype selectivity, such as prazosin and tamsulosin, to treat hypertension and benign prostatic hyperplasia, respectively. Abundant expression of α1-ARs in the heart and central nervous system (CNS) makes these receptors potential targets for the treatment of cardiovascular and CNS disorders, such as heart failure, epilepsy, and Alzheimer's disease. Our understanding of the precise physiological roles of α1-ARs, however, and their involvement in disease has been hindered by the lack of sufficiently subtype-selective tool compounds, especially for α1B-AR. Here, we report the discovery of 4-[(2-hydroxyethyl)amino]-6-methyl-2H-chromen-2-one (Cpd1), as an α1B-AR antagonist that has 10-15-fold selectivity over α1A-AR and α1D-AR. Through computational and site-directed mutagenesis studies, we have identified the binding site of Cpd1 in α1B-AR and propose the molecular basis of α1B-AR selectivity, where the nonconserved V19745.52 residue plays a major role, with contributions from L3146.55 within the α1B-AR pocket. By exploring the structure-activity relationships of Cpd1 at α1B-AR, we have also identified 3-[(cyclohexylamino)methyl]-6-methylquinolin-2(1H)-one (Cpd24), which has a stronger binding affinity than Cpd1, albeit with reduced selectivity for α1B-AR. Cpd1 and Cpd24 represent potential leads for α1B-AR-selective drug discovery and novel tool molecules to further study the physiology of α1-ARs.

An RNA replicon system to investigate promising inhibitors of feline coronavirus.

Feline infectious peritonitis (FIP) is a fatal feline disease, caused by a feline coronavirus (FCoV), namely feline infectious peritonitis virus (FIPV). We produced a baby hamster kidney 21 (BHK) cell line expressing a serotype I FCoV replicon RNA with a green fluorescent protein (GFP) reporter gene (BHK-F-Rep) and used it as an in vitro screening system to test different antiviral compounds. Two inhibitors of the FCoV main protease (Mpro), namely GC376 and Nirmatrelvir, as well as the nucleoside analog Remdesivir proved to be effective in inhibiting the replicon system. Different combinations of these compounds also proved to be potent inhibitors, having an additive effect when combined. Remdesivir, GC376, and Nirmatrelvir all have a 50% cytotoxic concentration (CC50) more than 200 times higher than their half-maximal inhibitory concentrations (IC50), making them important candidates for future in vivo studies as well as clinically implemented drug candidates. In addition, results were acquired with a virus infection system, where Felis catus whole fetus 4 (Fcwf-4) cells were infected with a previously described recombinant GFP-expressing FIPV (based on the laboratory-adapted serotype I FIPV strain Black) and treated with the most promising compounds. Results acquired with the replicon system were comparable to the results acquired with the virus infection system, demonstrating that we successfully implemented the FCoV replicon system for antiviral screening. We expect that this system will greatly facilitate future screens for anti-FIPV compounds and provide a non-infectious system to study and evaluate drug-resistant mutations that may emerge in the FIPV genome.IMPORTANCEFIPV is of great significance in the cat population around the world, causing 0.3%-1.4% of feline deaths in veterinary practices (2). As there are neither effective preventive measures nor approved treatment options available, there is an urgent need to identify antiviral drugs against FIPV. Our FCoV replicon system provides a valuable tool for drug discovery in vitro. Due to the lack of cell culture systems for serotype I FCoVs (the serotype most prevalent in the feline population) (2), a different system is needed to study these viruses. A viral replicon system is a valuable tool for studying FCoVs. Overall, our results demonstrate the utility of the serotype I feline coronavirus replicon system for antiviral screening as well as to study this virus in general. We propose several compounds representing promising candidates for future clinical trials and ultimately with the potential to save cats suffering from FIP.

BioisoIdentifier: an online free tool to investigate local structural replacements from PDB

Within the realm of contemporary medicinal chemistry, bioisosteres are empirically used to enhance potency and selectivity, improve adsorption, distribution, metabolism, excretion and toxicity profiles of drug candidates. It is believed that bioisosteric know-how may help bypass granted patents or generate novel intellectual property for commercialization. Beside the synthetic expertise, the drug discovery process also depends on efficient in silico tools. We hereby present BioisoIdentifier (BII), a web server aiming to uncover bioisosteric information for specific fragment. Using the Protein Data Bank as source, and specific substructures that the user attempt to surrogate as input, BII tries to find suitable fragments that fit well within the local protein active site. BII is a powerful computational tool that offers the ligand design ideas for bioisosteric replacing. For the validation of BII, catechol is conceived as model fragment attempted to be replaced, and many ideas are successfully offered. These outputs are hierarchically grouped according to structural similarity, and clustered based on unsupervised machine learning algorithms. In summary, we constructed a user-friendly interface to enable the viewing of top-ranking molecules for further experimental exploration. This makes BII a highly valuable tool for drug discovery. The BII web server is freely available to researchers and can be accessed at http://www.aifordrugs.cn/index/. Scientific Contribution: By designing a more optimal computational process for mining bioisosteric replacements from the publicly accessible PDB database, then deployed on a web server for throughly free access for researchers. Additionally, machine learning methods are applied to cluster the bioisosteric replacements searched by the platform, making a scientific contribution to facilitate chemists’ selection of appropriate bioisosteric replacements. The number of bioisosteric replacements obtained using BII is significantly larger than the currently available platforms, which expanding the search space for effective local structural replacements.Graphical

CRD: A de novo design algorithm for the prediction of cognate protein receptors for small molecule ligands

While predicting a ligand that binds to a protein is feasible with current methods, the opposite, i.e., the prediction of a receptor for a ligand remains challenging. We present an approach for predicting receptors of a given ligand that uses de novo design and structural bioinformatics. We have developed the algorithm CRD, comprising multiple modules combining fragment-based sub-site finding, a machine learning function to estimate the size of the site, a genetic algorithm that encodes knowledge on protein structures and a physics-based fitness scoring scheme. CRD includes a pseudo-receptor design component followed by a mapping component to identify proteins that might contain these sites. CRD recovers the sites and receptors of several natural ligands. It designs similar sites for similar ligands, yet to some extent can distinguish between closely related ligands. CRD correctly predicts receptor classes for several drugs and might become a valuable tool for drug discovery.

Discovery and Optimization of WDR5 Inhibitors via Cascade Deoxyribonucleic Acid-Encoded Library Selection Approach.

The DNA-encoded library (DEL) is a powerful hit generation tool for chemical biology and drug discovery; however, the optimization of DEL hits remained a daunting challenge for the medicinal chemistry community. In this study, hit compounds targeting the WIN binding domain of WDR5 were discovered by the initial three-cycle linear DEL selection, and their potency was further enhanced by a cascade DEL selection from the focused DEL designed based on the original first run DEL hits. As expected, these new compounds from the second run of focused DEL were more potent WDR5 inhibitors in the protein binding assay confirmed by the off-DNA synthesis. Interestingly, selected inhibitors exhibited good antiproliferative activity in two human acute leukemia cell lines. Taken together, this new cascade DEL selection strategy may have tremendous potential for finding high-affinity leads against WDR5 and provide opportunities to explore and optimize inhibitors for other targets.

Prodrugs as empowering tools in drug discovery and development: recent strategic applications of drug delivery solutions to mitigate challenges associated with lead compounds and drug candidates.

The delivery of a drug to a specific organ or tissue at an efficacious concentration is the pharmacokinetic (PK) hallmark of promoting effective pharmacological action at a target site with an acceptable safety profile. Sub-optimal pharmaceutical or ADME profiles of drug candidates, which can often be a function of inherently poor physicochemical properties, pose significant challenges to drug discovery and development teams and may contribute to high compound attrition rates. Medicinal chemists have exploited prodrugs as an informed strategy to productively enhance the profiles of new chemical entities by optimizing the physicochemical, biopharmaceutical, and pharmacokinetic properties as well as selectively delivering a molecule to the site of action as a means of addressing a range of limitations. While discovery scientists have traditionally employed prodrugs to improve solubility and membrane permeability, the growing sophistication of prodrug technologies has enabled a significant expansion of their scope and applications as an empowering tool to mitigate a broad range of drug delivery challenges. Prodrugs have emerged as successful solutions to resolve non-linear exposure, inadequate exposure to support toxicological studies, pH-dependent absorption, high pill burden, formulation challenges, lack of feasibility of developing solid and liquid dosage forms, first-pass metabolism, high dosing frequency translating to reduced patient compliance and poor site-specific drug delivery. During the period 2012-2022, the US Food and Drug Administration (FDA) approved 50 prodrugs, which amounts to 13% of approved small molecule drugs, reflecting both the importance and success of implementing prodrug approaches in the pursuit of developing safe and effective drugs to address unmet medical needs.

Application of PROTACs in Target Identification and Target Validation.

PROTAC, as a novel therapeutic drug model, has received widespread attention from the academic and pharmaceutical industries. At the same time, PROTAC technology has led many researchers to focus on developing chemical biology tool properties due to its unique operating mechanism and protein dynamic regulatory properties. In recent years, the rapid development of PROTAC technology has gradually made it an essential tool for target identification and target validation. To further promote the application of PROTAC tools in drug discovery and basic medical sciences research, this review distinguished between target identification and target validation concepts. It summarized the research progress of PROTAC technology in these aspects.

Gastrointestinal tract organoids as novel tools in drug discovery.

Organoids, characterized by their high physiological attributes, effectively preserve the genetic characteristics, physiological structure, and function of the simulated organs. Since the inception of small intestine organoids, other organoids for organs including the liver, lungs, stomach, and pancreas have subsequently been developed. However, a comprehensive summary and discussion of research findings on gastrointestinal tract (GIT) organoids as disease models and drug screening platforms is currently lacking. Herein, in this review, we address diseases related to GIT organoid simulation and highlight the notable advancements that have been made in drug screening and pharmacokinetics, as well as in disease research and treatment using GIT organoids. Organoids of GIT diseases, including inflammatory bowel disease, irritable bowel syndrome, necrotizing enterocolitis, and Helicobacter pylori infection, have been successfully constructed. These models have facilitated the study of the mechanisms and effects of various drugs, such as metformin, Schisandrin C, and prednisolone, in these diseases. Furthermore, GIT organoids have been used to investigate viruses that elicit GIT reactions, including Norovirus, SARS-CoV-2, and rotavirus. Previous studies by using GIT organoids have shown that dasabuvir, gemcitabine, and imatinib possess the capability to inhibit viral replication. Notably, GIT organoids can mimic GIT responses to therapeutic drugs at the onset of disease. The GIT toxicities of compounds like gefitinib, doxorubicin, and sunset yellow have also been evaluated. Additionally, these organoids are instrumental for the study of immune regulation, post-radiation intestinal epithelial repair, treatment for cystic fibrosis and diabetes, the development of novel drug delivery systems, and research into the GIT microbiome. The recent use of conditioned media as a culture method for replacing recombinant hepatocyte growth factor has significantly reduced the cost associated with human GIT organoid culture. This advancement paves the way for large-scale culture and compound screening of GIT organoids. Despite the ongoing challenges in GIT organoid development (e.g., their inability to exist in pairs, limited cell types, and singular drug exposure mode), these organoids hold considerable potential for drug screening. The use of GIT organoids in this context holds great promises to enhance the precision of medical treatments for patients living with GIT diseases.

Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK.

Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.

Development of a 3D printed perfusable in vitro blood-brain barrier model for use as a scalable screening tool.

Despite recent technological advances in drug discovery, the success rate for neurotherapeutics remains alarmingly low compared to treatments for other areas of the body. One of the biggest challenges for delivering therapeutics to the central nervous system (CNS) is the presence of the blood-brain barrier (BBB). In vitro blood-brain barrier models with high predictability are essential to aid in designing parameters for new therapeutics, assess their ability to cross the BBB, and investigate therapeutic strategies that can be employed to enhance transport. Here, we demonstrate the development of a 3D printable hydrogel blood-brain barrier model that mimics the cellular composition and structure of the blood-brain barrier with human brain endothelial cells lining the surface, pericytes in direct contact with the endothelial cells on the abluminal side of the endothelium, and astrocytes in the surrounding printed bulk matrix. We introduce a simple, static printed hemi-cylinder model to determine design parameters such as media selection, co-culture ratios, and cell incorporation timing in a resource-conservative and high-throughput manner. Presence of cellular adhesion junction, VE-Cadherin, efflux transporters, P-glycoprotein (P-gp) and Breast cancer resistance protein (BCRP), and receptor-mediated transporters, Transferrin receptor (TfR) and low-density lipoprotein receptor-related protein 1 (LRP1) were confirmed via immunostaining demonstrating the ability of this model for screening in therapeutic strategies that rely on these transport systems. Design parameters determined in the hemi-cylinder model were translated to a more complex, perfusable vessel model to demonstrate its utility for determining barrier function and assessing permeability to model therapeutic compounds. This 3D-printed blood-brain barrier model represents one of the first uses of projection stereolithography to fabricate a perfusable blood-brain barrier model, enabling the patterning of complex vessel geometries and precise arrangement of cell populations. This model demonstrates potential as a new platform to investigate the delivery of neurotherapeutic compounds and drug delivery strategies through the blood-brain barrier, providing a useful in vitro screening tool in central nervous system drug discovery and development.

Ultra-efficient delivery of CRISPR/Cas9 using ionic liquid conjugated polymers for genome editing-based tumor therapy.

Emerging CRISPR-Cas9 systems can rebuild DNA sequences in the genome in a spatiotemporal manner, offering a magic tool for biological research, drug discovery, and gene therapy. However, low delivery efficiency remains a major roadblock hampering the wide application of CRISPR-Cas9 gene editing talent. Herein, ionic liquid-conjugated polymers (IL-CPs) are explored as efficient platforms for CRISPR-Cas9 plasmid delivery and in vivo genome editing-based tumor therapy. Via molecular screening of IL-CPs, IL-CPs integrated with fluorination monomers (PBF) can encapsulate plasmids into hybrid nanoparticles and achieve over 90% delivery efficiency in various cells regardless of serum interference. In vitro and in vivo experiments demonstrate that PBF can mediate Cas9/PLK1 plasmids for intracellular delivery and therapeutic genome editing in tumor, achieving efficient tumor suppression. This work provides a new tool for safe and efficient CRISPR-Cas9 delivery and therapeutic genome editing, thus opening a new avenue for the development of ionic liquid polymeric vectors for genome editing and therapy.