Structure-based druggability assessment--identifying suitable targets for small molecule therapeutics

In vivo manufacture and manipulation of CAR-T cells for better druggability

The current CAR-T cell therapy products have been hampered in their druggability due to the personalized preparation required, unclear pharmacokinetic characteristics, and unpredictable adverse reactions. Enabling standardized manufacturing and having clear efficacy and pharmacokinetic characteristics are prerequisites for ensuring the effective practicality of CAR-T cell therapy drugs. This review provides a broad overview of the different approaches for controlling behaviors of CAR-T cells in vivo. The utilization of genetically modified vectors enables in vivo production of CAR-T cells, thereby abbreviating or skipping the lengthy in vitro expansion process. By equipping CAR-T cells with intricately designed control elements, using molecule switches or small-molecule inhibitors, the control of CAR-T cell activity can be achieved. Moreover, the on-off control of CAR-T cell activity would yield potential gains in phenotypic remodeling. These methods provide beneficial references for the future development of safe, controllable, convenient, and suitable for standardized production of CAR-T cell therapy products.

MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism

Accurate identification of potential drug-target pairs is a crucial step in drug development and drug repositioning, which is characterized by the ability of the drug to bind to and modulate the activity of the target molecule, resulting in the desired therapeutic effect. As machine learning and deep learning technologies advance, an increasing number of models are being engaged for the prediction of drug-target interactions. However, there is still a great challenge to improve the accuracy and efficiency of predicting. In this study, we proposed a deep learning method called Multi-source Information Fusion and Attention Mechanism for Drug-Target Interaction (MIFAM-DTI) to predict drug-target interactions. Firstly, the physicochemical property feature vector and the Molecular ACCess System molecular fingerprint feature vector of a drug were extracted based on its SMILES sequence. The dipeptide composition feature vector and the Evolutionary Scale Modeling -1b feature vector of a target were constructed based on its amino acid sequence information. Secondly, the PCA method was employed to reduce the dimensionality of the four feature vectors, and the adjacency matrices were constructed by calculating the cosine similarity. Thirdly, the two feature vectors of each drug were concatenated and the two adjacency matrices were subjected to a logical OR operation. And then they were fed into a model composed of graph attention network and multi-head self-attention to obtain the final drug feature vectors. With the same method, the final target feature vectors were obtained. Finally, these final feature vectors were concatenated, which served as the input to a fully connected layer, resulting in the prediction output. MIFAM-DTI not only integrated multi-source information to capture the drug and target features more comprehensively, but also utilized the graph attention network and multi-head self-attention to autonomously learn attention weights and more comprehensively capture information in sequence data. Experimental results demonstrated that MIFAM-DTI outperformed state-of-the-art methods in terms of AUC and AUPR. Case study results of coenzymes involved in cellular energy metabolism also demonstrated the effectiveness and practicality of MIFAM-DTI. The source code and experimental data for MIFAM-DTI are available at https://github.com/Search-AB/MIFAM-DTI.

Structure-based virtual screening study for identification of potent insecticides against Anopheles gambiae to combat the malaria

BACKGROUND OBJECTIVES

Vector-borne infectious diseases contribute significantly to global mortality, with over 700,000 annual deaths, and malaria alone accounts for more than 400,000 of these fatalities. Anopheles gambiae, a prominent mosquito species, serves as a primary vector for transmitting malaria to humans. To address this issue, researchers have identified the D1-like dopamine receptor (DAR), specifically DOP2, as a promising target for developing new insecticides.

METHODS

The three-dimensional structure of DOP2 from A. gambiae was unavailable; in-silico approach was used to model and validate DOP2 structure. The Discovery Studio 2021 program was used to identify potential binding sites on DOP2. Virtual screening of 235 anti-parasitic compounds was performed against DOP2 using PyRx 0.8.

RESULTS

The screening demonstrated strong binding and interactions with active site residues of DOP2 for five compounds: Diclazuril, Kaempferol, Deracoxib, Clindamycin, and Diaveridine. These compounds exhibited higher binding affinity values compared to the control (Asenapine). In addition, the predicted physiochemical properties for these compounds were within acceptable ranges and there were no violations in drug-likeness properties.

INTERPRETATION CONCLUSION

These compounds show promise as potential new insecticides targeting A. gambiae mosquito by inhibiting the DOP2 protein. However, additional experimental validation is required to optimize their efficacy as DOP2 inhibitors.

CRAFT: a web-integrated cavity prediction tool based on flow transfer algorithm

Numerous computational methods, including evolutionary-based, energy-based, and geometrical-based methods, are utilized to identify cavities inside proteins. Cavity information aids protein function annotation, drug design, poly-pharmacology, and allosteric site investigation. This article introduces "flow transfer algorithm" for rapid and effective identification of diverse protein cavities through multidimensional cavity scan. Initially, it identifies delimiter and susceptible tetrahedra to establish boundary regions and provide seed tetrahedra. Seed tetrahedron faces are precisely scanned using the maximum circle radius to transfer seed flow to neighboring tetrahedra. Seed flow continues until terminated by boundaries or forbidden faces, where a face is forbidden if the estimated maximum circle radius is less or equal to the user-defined maximum circle radius. After a seed scanning, tetrahedra involved in the flow are clustered to locate the cavity. The CRAFT web interface integrates this algorithm for protein cavity identification with enhanced user control. It supports proteins with cofactors, hydrogens, and ligands and provides comprehensive features such as 3D visualization, cavity physicochemical properties, percentage contribution graphs, and highlighted residues for each cavity. CRAFT can be accessed through its web interface at http://pitools.niper.ac.in/CRAFT , complemented by the command version available at https://github.com/PGlab-NIPER/CRAFT/ .Scientific contribution: Flow transfer algorithm is a novel geometric approach for accurate and reliable prediction of diverse protein cavities. This algorithm employs a distinct concept involving maximum circle radius within the 3D Delaunay triangulation to address diverse van der Waals radii while existing methods overlook atom specific van der Waals radii or rely on complex weighted geometric techniques.

RNA therapeutics history and future perspectives

Ribonucleic acid (RNA) therapeutics have significantly used RNA-based drugs to the prevention and treatment of diseases as effective messenger RNA-based vaccines in response to the COVID-19 pandemic. The RNA therapeutics with five classes including antisense oligonucleotide, small interfering RNA, microRNA, APTAMER and messenger RNAs are being quickly developed to treat various human diseases as neurological disease, cardiovascular disease, genetic and rare disease, cancer disease, coronavirus disease… which cannot be treated by other conventional drugs as small molecule-based drugs and antibodies. Therefore, the discovery of these RNA therapeutics created a new avenue for treatment of various human diseases. This chapter demonstrates the history of important discoveries in RNA biology and their impact on key developments in RNA therapeutics as well as the advantages of RNA therapeutics; RNA therapeutics describes the action mechanisms and examples of RNA-based drugs approved for treatment of various disease; and RNA therapeutics discusses delivery methods for RNA therapeutics to target organs and cells. In conclusion, this chapter is designed to offer an updated important development and advance of RNA therapeutics for the prevention and treatment of various human diseases.

Fragment library screening by X-ray crystallography and binding site analysis on thioredoxin glutathione reductase of Schistosoma mansoni

Schistosomiasis is caused by parasites of the genus Schistosoma, which infect more than 200 million people. Praziquantel (PZQ) has been the main drug for controlling schistosomiasis for over four decades, but despite that it is ineffective against juvenile worms and size and taste issues with its pharmaceutical forms impose challenges for treating school-aged children. It is also important to note that PZQ resistant strains can be generated in laboratory conditions and observed in the field, hence its extensive use in mass drug administration programs raises concerns about resistance, highlighting the need to search for new schistosomicidal drugs. Schistosomes survival relies on the redox enzyme thioredoxin glutathione reductase (TGR), a validated target for the development of new anti-schistosomal drugs. Here we report a high-throughput fragment screening campaign of 768 compounds against S. mansoni TGR (SmTGR) using X-ray crystallography. We observed 49 binding events involving 35 distinct molecular fragments which were found to be distributed across 16 binding sites. Most sites are described for the first time within SmTGR, a noteworthy exception being the "doorstop pocket" near the NADPH binding site. We have compared results from hotspots and pocket druggability analysis of SmTGR with the experimental binding sites found in this work, with our results indicating only limited coincidence between experimental and computational results. Finally, we discuss that binding sites at the doorstop/NADPH binding site and in the SmTGR dimer interface, should be prioritized for developing SmTGR inhibitors as new antischistosomal drugs.

Targeting the Structural Maturation Pathway of HIV-1 Reverse Transcriptase

Formation of active HIV-1 reverse transcriptase (RT) proceeds via a structural maturation process that involves subdomain rearrangements and formation of an asymmetric p66/p66' homodimer. These studies were undertaken to evaluate whether the information about this maturation process can be used to identify small molecule ligands that retard or interfere with the steps involved. We utilized the isolated polymerase domain, p51, rather than p66, since the initial subdomain rearrangements are largely limited to this domain. Target sites at subdomain interfaces were identified and computational analysis used to obtain an initial set of ligands for screening. Chromatographic evaluations of the p51 homodimer/monomer ratio support the feasibility of this approach. Ligands that bind near the interfaces and a ligand that binds directly to a region of the fingers subdomain involved in subunit interface formation were identified, and the interactions were further characterized by NMR spectroscopy and X-ray crystallography. Although these ligands were found to reduce dimer formation, further efforts will be required to obtain ligands with higher binding affinity. In contrast with previous ligand identification studies performed on the RT heterodimer, subunit interface surfaces are solvent-accessible in the p51 and p66 monomers, making these constructs preferable for identification of ligands that directly interfere with dimerization.

Mining gene expression data for rational identification of novel drug targets and vaccine candidates against the cattle tick, Rhipicephalus microplus

Control of complex parasites via vaccination remains challenging, with the current combination of vaccines and small drugs remaining the choice for an integrated control strategy. Studies conducted to date, are providing evidence that multicomponent vaccines will be needed for the development of protective vaccines against endo- and ectoparasites, though multicomponent vaccines require an in-depth understanding of parasite biology which remains insufficient for ticks. With the rapid development and spread of acaricide resistance in ticks, new targets for acaricide development also remains to be identified, along with novel targets that can be exploited for the design of lead compounds. In this study, we analysed the differential gene expression of Rhipicephalus microplus ticks that were fed on cattle vaccinated with a multi-component vaccine (Bm86 and 3 putative Bm86-binding proteins). The data was scrutinised for the identification of vaccine targets, small drug targets and novel pathways that can be evaluated in future studies. Limitations associated with targeting novel proteins for vaccine and/or drug design is also discussed and placed into the context of challenges arising when targeting large protein families and intracellular localised proteins. Lastly, this study provide insight into how Bm86-based vaccines may reduce successful uptake and digestion of the bloodmeal and overall tick fecundity.

VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder

MOTIVATION

Accurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing. (i) Traditional methods do not consider the use of multi-source data and do not consider the complex relationship between data sources. (ii) How to better mine the hidden features of drug and target space from high-dimensional data, and better solve the accuracy and robustness of the model.

RESULTS

To solve the above problems, a novel prediction model named VGAEDTI is proposed in this paper. We constructed a heterogeneous network with multiple sources of information using multiple types of drug and target dataIn order to obtain deeper features of drugs and targets, we use two different autoencoders. One is variational graph autoencoder (VGAE) which is used to infer feature representations from drug and target spaces. The second is graph autoencoder (GAE) propagating labels between known DTIs. Experimental results on two public datasets show that the prediction accuracy of VGAEDTI is better than that of six DTIs prediction methods. These results indicate that model can predict new DTIs and provide an effective tool for accelerating drug development and repurposing.

Epigenetic targets in B- and T-cell lymphomas: latest developments

Non-Hodgkin's lymphomas (NHLs) comprise a diverse group of diseases, either of mature B-cell or of T-cell derivation, characterized by heterogeneous molecular features and clinical manifestations. While most of the patients are responsive to standard chemotherapy, immunotherapy, radiation and/or stem cell transplantation, relapsed and/or refractory cases still have a dismal outcome. Deep sequencing analysis have pointed out that epigenetic dysregulations, including mutations in epigenetic enzymes, such as chromatin modifiers and DNA methyltransferases (DNMTs), are prevalent in both B- cell and T-cell lymphomas. Accordingly, over the past decade, a large number of epigenetic-modifying agents have been developed and introduced into the clinical management of these entities, and a few specific inhibitors have already been approved for clinical use. Here we summarize the main epigenetic alterations described in B- and T-NHL, that further supported the clinical development of a selected set of epidrugs in determined diseases, including inhibitors of DNMTs, histone deacetylases (HDACs), and extra-terminal domain proteins (bromodomain and extra-terminal motif; BETs). Finally, we highlight the most promising future directions of research in this area, explaining how bioinformatics approaches can help to identify new epigenetic targets in B- and T-cell lymphoid neoplasms.

Targeting RNA with small molecules: lessons learned from Xist RNA

Although more than 98% of the human genome is noncoding, nearly all drugs on the market target one of about 700 disease-related proteins. However, an increasing number of diseases are now being attributed to noncoding RNA and the ability to target them would vastly expand the chemical space for drug development. We recently devised a screening strategy based upon affinity-selection mass spectrometry and succeeded in identifying bioactive compounds for the noncoding RNA prototype, Xist. One such compound, termed X1, has drug-like properties and binds specifically to the RepA motif of Xist in vitro and in vivo. Small-angle X-ray scattering analysis reveals that X1 changes the conformation of RepA in solution, thereby explaining the displacement of cognate interacting protein factors (PRC2 and SPEN) and inhibition of X-chromosome inactivation. In this Perspective, we discuss lessons learned from these proof-of-concept experiments and suggest that RNA can be systematically targeted by drug-like compounds to disrupt RNA structure and function.

Trends in oral small-molecule drug discovery and product development based on product launches before and after the Rule of Five

In 1997, the 'Rule of Five' (Ro5) suggested physicochemical limitations for orally administered drugs, based on the analysis of chemical libraries from the early 1990s. In this review, we report on the trends in oral drug product development by analyzing products launched between 1994 and 1997 and between 2013 and 2019. Our analysis confirmed that most new oral drugs are within the Ro5 descriptors; however, the number of new drug products of drugs with molecular weight (MW) and calculated partition coefficient (clogP) beyond the Ro5 has slightly increased. Analysis revealed that there is no single scientific or technological reason for this trend, but that it likely results from incremental advances are being made in molecular biology, target diversity, drug design, medicinal chemistry, predictive modeling, drug metabolism and pharmacokinetics, and drug delivery.

Rationally designed inhibitors of the Musashi protein-RNA interaction by hotspot mimicry

RNA-binding proteins (RBPs) are key post-transcriptional regulators of gene expression, and thus underlie many important biological processes. Here, we developed a strategy that entails extracting a "hotspot pharmacophore" from the structure of a protein-RNA complex, to create a template for designing small-molecule inhibitors and for exploring the selectivity of the resulting inhibitors. We demonstrate this approach by designing inhibitors of Musashi proteins MSI1 and MSI2, key regulators of mRNA stability and translation that are upregulated in many cancers. We report this novel series of MSI1/MSI2 inhibitors is specific and active in biochemical, biophysical, and cellular assays. This study extends the paradigm of "hotspots" from protein-protein complexes to protein-RNA complexes, supports the "druggability" of RNA-binding protein surfaces, and represents one of the first rationally-designed inhibitors of non-enzymatic RNA-binding proteins. Owing to its simplicity and generality, we anticipate that this approach may also be used to develop inhibitors of many other RNA-binding proteins; we also consider the prospects of identifying potential off-target interactions by searching for other RBPs that recognize their cognate RNAs using similar interaction geometries. Beyond inhibitors, we also expect that compounds designed using this approach can serve as warheads for new PROTACs that selectively degrade RNA-binding proteins.

Non-coding RNAs in stroke pathology, diagnostics, and therapeutics

Ischemic stroke is a leading cause of death and disability worldwide. Methods to alleviate functional deficits after ischemic stroke focus on restoration of cerebral blood flow to the affected area. However, pharmacological or surgical methods such as thrombolysis and thrombectomy have a narrow effective window. Harnessing and manipulating neurochemical processes of recovery may provide an alternative to these methods. Recently, non-coding RNA (ncRNA) have been increasingly investigated for their contributions to the pathology of diseases and potential for diagnostic and therapeutic applications. Here we will review several ncRNA - H19, MALAT1, ANRIL, NEAT1, pseudogenes, small nucleolar RNA, piwi-interacting RNA and circular RNA - and their involvement in stroke pathology. We also examine these ncRNA as potential diagnostic biomarkers, particularly in circulating blood, and as targets for therapeutic interventions. An important aspect of this is a discussion of potential methods of treatment delivery to allow for targeting of interventions past the blood-brain barrier, including lipid nanoparticles, polymer nanoparticles, and viral and non-viral vectors. Overall, several long non-coding RNA (lncRNA) discussed here have strong implications for the development of pathology and functional recovery after ischemic stroke. LncRNAs H19 and ANRIL show potential as diagnostic biomarkers, while H19 and MALAT1 may prove to be effective therapeutics for both minimising damage as well as promoting recovery. Other ncRNA have also been implicated in ischemic stroke but are currently too poorly understood to make inferences for diagnosis or treatment. Whilst the field of ncRNAs is relatively new, significant work has already highlighted that ncRNAs represent a promising novel investigative tool for understanding stroke pathology, could be used as diagnostic biomarkers, and as targets for therapeutic interventions.

Druggable protein prediction using a multi-canal deep convolutional neural network based on autocovariance method

Drug targets must be identified and positioned correctly to research and manufacture new drugs. In this study, rather than using traditional methods for drug expansion, the drug target is determined using machine learning. Machine learning has generated significant interest and desire in recent years and extensive research due to its low cost and speed of operation. As a result, it is critical to develop an intelligent classification system for drug proteins. This study proposes two distinct models for the prediction of druggable protein classes based on the deep learning method. The translation of drug-protein sequences is based on six physicochemical properties of amino acids. Following the application of the autocovariance method, converted sequences are used as fixed-length input vectors in deep stacked sparse auto-encoders (DSSAEs) network. The coded protein sequences are also considered and utilized as a six-channel input vector for the deep convolutional neural network model. The experimental results contributing to the deep convolution model are more efficient than previous studies for classifying druggable proteins. The proposed approach achieved a sensitivity of 96.92%, a specificity of 99.51%, and an accuracy of 98.29%.

Increased slow dynamics defines ligandability of BTB domains

Efficient determination of protein ligandability, or the propensity to bind small-molecules, would greatly facilitate drug development for novel targets. Ligandability is currently assessed using computational methods that typically consider the static structural properties of putative binding sites or by experimental fragment screening. Here, we evaluate ligandability of conserved BTB domains from the cancer-relevant proteins LRF, KAISO, and MIZ1. Using fragment screening, we discover that MIZ1 binds multiple ligands. However, no ligands are uncovered for the structurally related KAISO or LRF. To understand the principles governing ligand-binding by BTB domains, we perform comprehensive NMR-based dynamics studies and find that only the MIZ1 BTB domain exhibits backbone µs-ms time scale motions. Interestingly, residues with elevated dynamics correspond to the binding site of fragment hits and recently defined HUWE1 interaction site. Our data argue that examining protein dynamics using NMR can contribute to identification of cryptic binding sites, and may support prediction of the ligandability of novel challenging targets.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

Shedding Light on the Drug-Target Prediction of the Anti-Inflammatory Peptide TnP with Bioinformatics Tools

Peptide–protein interactions are involved in various fundamental cellular functions, and their identification is crucial for designing efficacious peptide therapeutics. Drug–target interactions can be inferred by in silico prediction using bioinformatics and computational tools. We patented the TnP family of synthetic cyclic peptides, which is in the preclinical stage of developmental studies for chronic inflammatory diseases such as multiple sclerosis. In an experimental autoimmune enceph-alomyelitis model, we found that TnP controls neuroinflammation and prevents demyelination due to its capacity to cross the blood–brain barrier and to act in the central nervous system blocking the migration of inflammatory cells responsible for neuronal degeneration. Therefore, the identification of potential targets for TnP is the objective of this research. In this study, we used bioinformatics and computational approaches, as well as bioactivity databases, to evaluate TnP–target prediction for proteins that were not experimentally tested, specifically predicting the 3D structure of TnP and its biochemical characteristics, TnP–target protein binding and docking properties, and dynamics of TnP competition for the protein/receptor complex interaction, construction of a network of con-nectivity and interactions between molecules as a result of TnP blockade, and analysis of similarities with bioactive molecules. Based on our results, integrins were identified as important key proteins and considered responsible to regulate TnP-governed pharmacological effects. This comprehensive in silico study will help to understand how TnP induces its anti-inflammatory effects and will also facilitate the identification of possible side effects, as it shows its link with multiple biologically important targets in humans.

PointSite: A Point Cloud Segmentation Tool for Identification of Protein Ligand Binding Atoms

Accurate identification of ligand binding sites (LBS) on a protein structure is critical for understanding protein function and designing structure-based drugs. As the previous pocket-centric methods are usually based on the investigation of pseudo-surface-points outside the protein structure, they cannot fully take advantage of the local connectivity of atoms within the protein, as well as the global 3D geometrical information from all the protein atoms. In this paper, we propose a novel point clouds segmentation method, PointSite, for accurate identification of protein ligand binding atoms, which performs protein LBS identification at the atom-level in a protein-centric manner. Specifically, we first transfer the original 3D protein structure to point clouds and then conduct segmentation through Submanifold Sparse Convolution based U-Net. With the fine-grained atom-level binding atoms representation and enhanced feature learning, PointSite can outperform previous methods in atom Intersection over Union (atom-IoU) by a large margin. Furthermore, our segmented binding atoms, that is, atoms with high probability predicted by our model can work as a filter on predictions achieved by previous pocket-centric approaches, which significantly decreases the false-positive of LBS candidates. Besides, we further directly extend PointSite trained on bound proteins for LBS identification on unbound proteins, which demonstrates the superior generalization capacity of PointSite. Through cascaded filter and reranking aided by the segmented atoms, state-of-the-art performance can be achieved over various canonical benchmarks, CAMEO hard targets, and unbound proteins in terms of the commonly used DCA criteria.

Is there a common allosteric binding site for G-protein coupled receptors?

Targeting the allosteric sites on G-protein coupled receptors (GPCRs) for drug discovery is attracting increased interest. Given a GPCR target, identifying the allosteric binding sites in it remains a challenge. Previous works from our and other labs suggest the intracellular region below the middle of the transmembrane (TM) domain that spatially overlaps with the G-protein binding site could contain a common allosteric site for all GPCRs. We performed several bioinformatics analyses on this site for more than 100 representative human GPCR structures. Results of the studies confirmed that the proposed region contains an allosteric site that is druggable for 89% of the GPCRs and is not 100% identical between a GPCR and its most similar homolog for 94% of the GPCRs. The physico-chemical properties and amino acid composition of this site vary among and within GPCR classes. Since this proposed region occupies the space existing in all GPCRs of known structure, it could represent a common host of an allosteric site for all GPCRs that can be targeted for structure-based allosteric drug design.

RNA therapy: rich history, various applications and unlimited future prospects

RNA therapy refers to the treatment or prevention of diseases using RNA-based molecules. The recent advent of a series of effective messenger RNA-based vaccines in response to the COVID-19 pandemic has reignited research interest in RNA therapy. Based on the accumulated results of long-term research in the field of RNA therapy spanning several decades, therapeutic agents for various diseases are being rapidly developed. These therapeutics tend to target diseases that cannot be treated with other conventional drug groups, and several clinical studies are underway for a variety of RNA-based therapeutics against various incurable diseases. This review describes the history of several important discoveries in RNA biology and their impact on key developments in RNA therapy as well as the advantages of RNA therapy. In addition, it describes the action mechanisms and examples of drugs approved for RNA therapy. Finally, this review discusses methods for RNA drug delivery to target organs and cells. Given that RNA therapy is expected to advance and play an integral role in the development of novel therapeutic agents for human diseases in the future, this review is designed to offer an updated reference point for researchers in this field.

RNA-based therapies should improve the lives of many people affected by difficult-to-treat diseases, provided novel drug delivery methods are fully explored and optimized. Young-Kook Kim at Chonnam National University Medical School in Hwasun, Korea, reviewed the current status of RNA therapies, particularly given recent successes in developing messenger RNA vaccines for COVID-19. RNA therapeutics work by manipulating the expression and activity of specific target molecules, providing the means to treat diseases that do not respond to conventional drug types. Such therapies can be tweaked to cover a wide range of different forms of RNA and protein, and could open the way to personalized medicines and treatments for rare diseases. However, RNA-based drugs are larger molecules than other therapeutics, making targeted delivery within the body more difficult. Kim suggests that ensuring effective RNA drug delivery should be a paramount focus for future research.

Transcriptome-Based Molecular Networks Uncovered Interplay Between Druggable Genes of CD8+ T Cells and Changes in Immune Cell Landscape in Patients With Pulmonary Tuberculosis

BACKGROUND

Tuberculosis (TB) is a major infectious disease, where incomplete information about host genetics and immune responses is hindering the development of transformative therapies. This study characterized the immune cell landscape and blood transcriptomic profile of patients with pulmonary TB (PTB) to identify the potential therapeutic biomarkers.

METHODS

The blood transcriptome profile of patients with PTB and controls were used for fractionating immune cell populations with the CIBERSORT algorithm and then to identify differentially expressed genes (DEGs) with R/Bioconductor packages. Later, systems biology investigations (such as semantic similarity, gene correlation, and graph theory parameters) were implemented to prioritize druggable genes contributing to the immune cell alterations in patients with TB. Finally, real time-PCR (RT-PCR) was used to confirm gene expression levels.

RESULTS

Patients with PTB had higher levels of four immune subpopulations like CD8⁺ T cells (P = 1.9 × 10^-8), natural killer (NK) cells resting (P = 6.3 × 10^-5), monocytes (P = 6.4 × 10^-6), and neutrophils (P = 1.6 × 10^-7). The functional enrichment of 624 DEGs identified in the blood transcriptome of patients with PTB revealed major dysregulation of T cell-related ontologies and pathways (q ≤ 0.05). Of the 96 DEGs shared between transcriptome and immune cell types, 39 overlapped with TB meta-profiling genetic signatures, and their semantic similarity analysis with the remaining 57 genes, yielded 45 new candidate TB markers. This study identified 9 CD8⁺ T cell-associated genes (ITK, CD2, CD6, CD247, ZAP70, CD3D, SH2D1A, CD3E, and IL7R) as potential therapeutic targets of PTB by combining computational druggability and co-expression (r² ≥ |0.7|) approaches.

CONCLUSION

The changes in immune cell proportion and the downregulation of T cell-related genes may provide new insights in developing therapeutic compounds against chronic TB.

Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach

Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles from gene perturbations. We present a novel computational approach for drug target discovery based on machine learning (ML) models. ML models are first trained on drug-induced expression profiles with outcomes defined as whether the drug treats the studied disease. The goal is to “learn” the expression patterns associated with treatment. Then, the fitted ML models were applied to expression profiles from gene perturbations (overexpression (OE)/knockdown (KD)). We prioritized targets based on predicted probabilities from the ML model, which reflects treatment potential. The methodology was applied to predict targets for hypertension, diabetes mellitus (DM), rheumatoid arthritis (RA), and schizophrenia (SCZ). We validated our approach by evaluating whether the identified targets may ‘re-discover’ known drug targets from an external database (OpenTargets). Indeed, we found evidence of significant enrichment across all diseases under study. A further literature search revealed that many candidates were supported by previous studies. For example, we predicted PSMB8 inhibition to be associated with the treatment of RA, which was supported by a study showing that PSMB8 inhibitors (PR-957) ameliorated experimental RA in mice. In conclusion, we propose a new ML approach to integrate the expression profiles from drugs and gene perturbations and validated the framework. Our approach is flexible and may provide an independent source of information when prioritizing drug targets.

The applications of deep learning algorithms on in silico druggable proteins identification

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We developed the first deep learning-based druggable protein classifier for fast and accurate identification of potential druggable proteins.

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Experimental results on a standard dataset demonstrate that the prediction performance of deep learning model is comparable to those of existing methods.

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We visualized the representations of druggable proteins learned by deep learning models, which helps us understand how they work.

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Our analysis reconfirms that the attention mechanism is especially useful for explaining deep learning models.

Introduction

The top priority in drug development is to identify novel and effective drug targets. In vitro assays are frequently used for this purpose; however, traditional experimental approaches are insufficient for large-scale exploration of novel drug targets, as they are expensive, time-consuming and laborious. Therefore, computational methods have emerged in recent decades as an alternative to aid experimental drug discovery studies by developing sophisticated predictive models to estimate unknown drugs/compounds and their targets. The recent success of deep learning (DL) techniques in machine learning and artificial intelligence has further attracted a great deal of attention in the biomedicine field, including computational drug discovery.

Objectives

This study focuses on the practical applications of deep learning algorithms for predicting druggable proteins and proposes a powerful predictor for fast and accurate identification of potential drug targets.

Methods

Using a gold-standard dataset, we explored several typical protein features and different deep learning algorithms and evaluated their performance in a comprehensive way. We provide an overview of the entire experimental process, including protein features and descriptors, neural network architectures, libraries and toolkits for deep learning modelling, performance evaluation metrics, model interpretation and visualization.

Results

Experimental results show that the hybrid model (architecture: CNN-RNN (BiLSTM) + DNN; feature: dictionary encoding + DC_TC_CTD) performed better than the other models on the benchmark dataset. This hybrid model was able to achieve 90.0% accuracy and 0.800 MCC on the test dataset and 84.8% and 0.703 on a nonredundant independent test dataset, which is comparable to those of existing methods.

Conclusion

We developed the first deep learning-based classifier for fast and accurate identification of potential druggable proteins. We hope that this study will be helpful for future researchers who would like to use deep learning techniques to develop relevant predictive models.

The PROTACtable genome

Proteolysis-targeting chimeras (PROTACs) are an emerging drug modality that may offer new opportunities to circumvent some of the limitations associated with traditional small-molecule therapeutics. By analogy with the concept of the 'druggable genome', the question arises as to which potential drug targets might PROTAC-mediated protein degradation be most applicable. Here, we present a systematic approach to the assessment of the PROTAC tractability (PROTACtability) of protein targets using a series of criteria based on data and information from a diverse range of relevant publicly available resources. Our approach could support decision-making on whether or not a particular target may be amenable to modulation using a PROTAC. Using our approach, we identified 1,067 proteins of the human proteome that have not yet been described in the literature as PROTAC targets that offer potential opportunities for future PROTAC-based efforts.

Designing small molecules for therapeutic success: A contemporary perspective

Successful small-molecule drug design requires a molecular target with inherent therapeutic potential and a molecule with the right properties to unlock its potential. Present-day drug design strategies have evolved to leave little room for improvement in drug-like properties. As a result, inadequate safety or efficacy associated with molecular targets now constitutes the primary cause of attrition in preclinical development through Phase II. This finding has led to a deeper focus on target selection. In this current reality, design tactics that enable rapid identification of risk-balanced clinical candidates, translation of clinical experience into meaningful differentiation strategies, and expansion of the druggable proteome represent significant levers by which drug designers can accelerate the discovery of the next generation of medicines.

Challenges in Drug Discovery for Intracellular Bacteria

Novel drugs are needed to treat a variety of persistent diseases caused by intracellular bacterial pathogens. Virulence pathways enable many functions required for the survival of these pathogens, including invasion, nutrient acquisition, and immune evasion. Inhibition of virulence pathways is an established route for drug discovery; however, many challenges remain. Here, we propose the biggest problems that must be solved to advance the field meaningfully. While it is established that we do not yet understand the nature of chemicals capable of permeating into the bacterial cell, this problem is compounded when targeting intracellular bacteria because we are limited to only those chemicals that can permeate through both human and bacterial outer envelopes. Unfortunately, many chemicals that permeate through the outer layers of mammalian cells fail to penetrate the bacterial cytoplasm. Another challenge is the lack of publicly available information on virulence factors. It is virtually impossible to know which virulence factors are clinically relevant and have broad cross-species and cross-strain distribution. In other words, we have yet to identify the best drug targets. Yes, standard genomics databases have much of the information necessary for short-term studies, but the connections with patient outcomes are yet to be established. Without comprehensive data on matters such as these, it is difficult to devise broad-spectrum, effective anti-virulence agents. Furthermore, anti-virulence drug discovery is hindered by the current state of technologies available for experimental investigation. Antimicrobial drug discovery was greatly advanced by the establishment and standardization of broth microdilution assays to measure the effectiveness of antimicrobials. However, the currently available models used for anti-virulence drug discovery are too broad, as they must address varied phenotypes, and too expensive to be generally adopted by many research groups. Therefore, we believe drug discovery against intracellular bacterial pathogens can be advanced significantly by overcoming the above hurdles.

In silico drug discovery for a complex immunotherapeutic target - human c-Rel protein

Target evaluation and rational drug design rely on identifying and characterising small-molecule binding sites on therapeutically relevant target proteins. Immunotherapeutics development is especially challenging because of complex disease etiology and heterogenous nature of targets. c-Rel protein, a promising target in many human inflammatory and cancer pathologies, was selected as a case study for an effective in silico screening platform development since this transcription factor currently has no successful therapeutic inhibitors or modulators. This study introduces a novel in silico screening approach to probe binding sites using structural validation sets, molecular modelling and describes a method of a computer-aided drug design when a crystal structure is not available for the target of interest. In addition, we showed that binding sites can be analysed with the machine learning as well as molecular simulation approaches to help assess and systematically analyse how drug candidates can exert their mode of action. Finally, this cutting-edge approach was subjected to a high through-put virtual screen of selected 34 M drug-like compounds filtered from a library of 659 M compounds by identifying the most promising structures and proposing potential action mechanisms for the future development of highly selective human c-Rel inhibitors and/or modulators.

Structure and Dynamics of Zika Virus Protease and Its Insights into Inhibitor Design

Zika virus (ZIKV)—a member of the Flaviviridae family—is an important human pathogen. Its genome encodes a polyprotein that can be further processed into structural and non-structural proteins. ZIKV protease is an important target for antiviral development due to its role in cleaving the polyprotein to release functional viral proteins. The viral protease is a two-component protein complex formed by NS2B and NS3. Structural studies using different approaches demonstrate that conformational changes exist in the protease. The structures and dynamics of this protease in the absence and presence of inhibitors were explored to provide insights into the inhibitor design. The dynamic nature of residues binding to the enzyme cleavage site might be important for the function of the protease. Due to the charges at the protease cleavage site, it is challenging to develop small-molecule compounds acting as substrate competitors. Developing small-molecule compounds to inhibit protease activity through an allosteric mechanism is a feasible strategy because conformational changes are observed in the protease. Herein, structures and dynamics of ZIKV protease are summarized. The conformational changes of ZIKV protease and other proteases in the same family are discussed. The progress in developing allosteric inhibitors is also described. Understanding the structures and dynamics of the proteases are important for designing potent inhibitors.

Druggability Assessment for Selected Serine Proteases in a Pharmaceutical Industry Setting

Target druggability assessment is an integral part of the early target characterization and selection process in pharmaceutical industry. Here, we investigate a set of five different serine proteases from the blood coagulation cascade. The aim of this study is twofold. Firstly, leveraging the wealth of available in-house high-throughput screening (HTS) data, we analyze HTS hit rates and discuss their predictive value for the development of small molecule (SMOL) candidates. Purely structure-activity relationship (SAR) based druggability ratings are compared with computational protein-structure based druggability assessments. Secondly, we evaluate the impact of using conformational ensembles from molecular dynamics (MD) simulations instead of single static crystal structures as basis for computational druggability assessments. Based on this study, we recommend incorporating molecular dynamics routinely into the early target characterization process, especially if only a single X-ray structure is available.

Solute Carrier Transporters as Potential Targets for the Treatment of Metabolic Disease

The solute carrier (SLC) superfamily comprises more than 400 transport proteins mediating the influx and efflux of substances such as ions, nucleotides, and sugars across biological membranes. Over 80 SLC transporters have been linked to human diseases, including obesity and type 2 diabetes (T2D). This observation highlights the importance of SLCs for human (patho)physiology. Yet, only a small number of SLC proteins are validated drug targets. The most recent drug class approved for the treatment of T2D targets sodium-glucose cotransporter 2, product of the SLC5A2 gene. There is great interest in identifying other SLC transporters as potential targets for the treatment of metabolic diseases. Finding better treatments will prove essential in future years, given the enormous personal and socioeconomic burden posed by more than 500 million patients with T2D by 2040 worldwide. In this review, we summarize the evidence for SLC transporters as target structures in metabolic disease. To this end, we identified SLC13A5/sodium-coupled citrate transporter, and recent proof-of-concept studies confirm its therapeutic potential in T2D and nonalcoholic fatty liver disease. Further SLC transporters were linked in multiple genome-wide association studies to T2D or related metabolic disorders. In addition to presenting better-characterized potential therapeutic targets, we discuss the likely unnoticed link between other SLC transporters and metabolic disease. Recognition of their potential may promote research on these proteins for future medical management of human metabolic diseases such as obesity, fatty liver disease, and T2D. SIGNIFICANCE STATEMENT: Given the fact that the prevalence of human metabolic diseases such as obesity and type 2 diabetes has dramatically risen, pharmacological intervention will be a key future approach to managing their burden and reducing mortality. In this review, we present the evidence for solute carrier (SLC) genes associated with human metabolic diseases and discuss the potential of SLC transporters as therapeutic target structures.

Endogenous toxic metabolites and implications in cancer therapy

It is well recognized that many metabolic enzymes play essential roles in cancer cells in producing building blocks such as nucleotides, which are required in greater amounts due to their increased proliferation. On the other hand, the significance of enzymes in preventing the accumulation of their substrates is less recognized. Here, we outline the evidence and underlying mechanisms for how many metabolites normally produced in cells are highly toxic, such as metabolites containing reactive groups (e.g., methylglyoxal, 4-hydroxynonenal, and glutaconyl-CoA), or metabolites that act as competitive analogs against other metabolites (e.g., deoxyuridine triphosphate and l-2-hydroxyglutarate). Thus, if a metabolic pathway contains a toxic intermediate, then we may be able to induce accumulation and poison a cancer cell by targeting the downstream enzyme. Furthermore, this poisoning may be cancer cell selective if this pathway is overactive in a cancer cell relative to a nontransformed cell. We describe this concept as illustrated in selenocysteine metabolism and other pathways and discuss future directions in exploiting toxic metabolites to kill cancer cells.

Mechanisms of Action for Small Molecules Revealed by Structural Biology in Drug Discovery

Small-molecule drugs are organic compounds affecting molecular pathways by targeting important proteins. These compounds have a low molecular weight, making them penetrate cells easily. Small-molecule drugs can be developed from leads derived from rational drug design or isolated from natural resources. A target-based drug discovery project usually includes target identification, target validation, hit identification, hit to lead and lead optimization. Understanding molecular interactions between small molecules and their targets is critical in drug discovery. Although many biophysical and biochemical methods are able to elucidate molecular interactions of small molecules with their targets, structural biology is the most powerful tool to determine the mechanisms of action for both targets and the developed compounds. Herein, we reviewed the application of structural biology to investigate binding modes of orthosteric and allosteric inhibitors. It is exemplified that structural biology provides a clear view of the binding modes of protease inhibitors and phosphatase inhibitors. We also demonstrate that structural biology provides insights into the function of a target and identifies a druggable site for rational drug design.

A Nonradioactive High-Throughput Screening-Compatible Cell-Based Assay to Identify Inhibitors of the Monocarboxylate Transporter Protein 1

Solute carrier proteins (SLCs) are a superfamily of transmembrane transporters that control essential physiological functions such as nutrient uptake, ion transport, and cellular waste elimination. Although many SLCs are associated with various disease states and are considered "druggable," they remain underexplored as a drug target class. One subfamily of SLCs that has gained attention for its therapeutic potential is the monocarboxylate solute transporter family. The monocarboxylate transporter protein 1 (MCT1) is a passive transporter of lactate and has gained significant attention for its role(s) in cancer progression; moreover, upregulation of MCT1 connotes poor patient outcome and survival. Consequently, small molecule inhibitors of MCT1 activity are being pursued as anticancer therapies. However, typical for members of this SLC subfamily, there is a paucity of potent and selective modulators of MCT1. This is in part due to methods used for their identification, typically relying on the use of radiolabeled substrate tracing. In addition to the safety concerns associated with radioactivity, this methodology is also expensive and time consuming. In this study, we describe the use of an MCT1 cytotoxic substrate as a tool to enable the development of a nonradioactive cell-based homogeneous assay that facilitates industry-scale high-throughput screening (HTS) of large compound libraries to identify novel MCT1 inhibitors to interrogate the therapeutic potential of MCT1. Our assay is robust, reproducible, HTS amenable, and establishes a conceptually novel way to identify chemical probes to investigate the therapeutic potential of SLC proteins.

Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say?

The drug discovery process typically involves target identification and design of suitable drug molecules against these targets. Despite decades of experimental investigations in the drug discovery domain, about 96% overall failure rate has been recorded in drug development due to the "undruggability" of various identified disease targets, in addition to other challenges. Likewise, the high attrition rate of drug candidates in the drug discovery process has also become an enormous challenge for the pharmaceutical industry. To alleviate this negative outlook, new trends in drug discovery have emerged. By drifting away from experimental research methods, computational tools and big data are becoming valuable in the prediction of biological target druggability and the drug-likeness of potential therapeutic agents. These tools have proven to be useful in saving time and reducing research costs. As with any emerging technique, however, controversial opinions have been presented regarding the validation of predictive computational tools. To address the challenges associated with these varying opinions, this review attempts to highlight the principles of druggability and drug-likeness and their recent advancements in the drug discovery field. Herein, we present the different computational tools and their reliability of predictive analysis in the drug discovery domain. We believe that this report would serve as a comprehensive guide towards computational-oriented drug discovery research. Graphical abstract Highlights of methods for assessing the druggability of biological targets.

New Insights from Crystallographic Data: Diversity of Structural Motifs and Molecular Recognition Properties between Groups of IDO1 Structures

A large number of crystallographic structures of IDO1 in different ligand-bound and -unbound states have been disclosed over the last decade. Yet, only a few of them have been exploited for structure-based drug design (SBDD) campaigns. In this study, we analyzed the structural motifs and molecular-recognition properties of three groups of IDO1 structures: 1) structures containing the heme group and inhibitors in the catalytic site; 2) heme-free structures of IDO1; 3) substrate-bound structures of IDO1. The results suggest that unrelated conformations of the enzyme have been solved with different ligand-induced changes of secondary motifs that localize even in regions remote from the catalytic site. Moreover, the study identified an uncharted region of molecular-recognition space covered by IDO1 binding sites that could guide the selection of diverse structures for additional SBDD studies aimed at the identification of novel lead compounds with differentiated chemical scaffolds.

Structure-based Druggability Assessment of Anti-virulence Targets from Pseudomonas aeruginosa

Antimicrobial Resistance (AMR) represents a serious threat to health and the global economy. However, interest in antibacterial drug development has decreased substantially in recent decades. Meanwhile, anti-virulence drug development has emerged as an attractive alternative to fight AMR. Although several macromolecular targets have been explored for this goal, their druggability is a vital piece of information that has been overlooked. This review explores this subject by showing how structure- based freely available in silico tools, such as PockDrug and FTMap, might be useful for designing novel inhibitors of the pyocyanin biosynthesis pathway and improving the potency/selectivity of compounds that target the Pseudomonas aeruginosa quorum sensing mechanism. The information provided by hotspot analysis, along with binding site features, reveals novel druggable targets (PhzA and PhzS) that remain largely unexplored. However, it also highlights that in silico druggability prediction tools have several limitations that might be overcome in the near future. Meanwhile, anti-virulence drug targets should be assessed by complementary methods, such as the combined use of FTMap/PockDrug, once the consensus druggability classification reduces the risk of wasting resources on undruggable proteins.

Haloperidol bound D2 dopamine receptor structure inspired the discovery of subtype selective ligands

The D₂ dopamine receptor (DRD2) is one of the most well-established therapeutic targets for neuropsychiatric and endocrine disorders. Most clinically approved and investigational drugs that target this receptor are known to be subfamily-selective for all three D₂-like receptors, rather than subtype-selective for only DRD2. Here, we report the crystal structure of DRD2 bound to the most commonly used antipsychotic drug, haloperidol. The structures suggest an extended binding pocket for DRD2 that distinguishes it from other D₂-like subtypes. A detailed analysis of the structures illuminates key structural determinants essential for DRD2 activation and subtype selectivity. A structure-based and mechanism-driven screening combined with a lead optimization approach yield DRD2 highly selective agonists, which could be used as chemical probes for studying the physiological and pathological functions of DRD2 as well as promising therapeutic leads devoid of promiscuity.

The D₂ dopamine receptor (DRD2) is one of the most well-established therapeutic targets for neuropsychiatric and endocrine disorders. Here, the authors report the crystal structure of the antipsychotic drug haloperidol bound to DRD2 via an extended binding pocket that distinguishes it from other D₂-like subtypes.

Structural dynamics and allostery of Rab proteins: strategies for drug discovery and design

Rab proteins represent the largest family of the Rab superfamily guanosine triphosphatase (GTPase). Aberrant human Rab proteins are associated with multiple diseases, including cancers and neurological disorders. Rab subfamily members display subtle conformational variations that render specificity in their physiological functions and can be targeted for subfamily-specific drug design. However, drug discovery efforts have not focused much on targeting Rab allosteric non-nucleotide binding sites which are subjected to less evolutionary pressures to be conserved, hence are likely to offer subfamily specificity and may be less prone to undesirable off-target interactions and side effects. To discover druggable allosteric binding sites, Rab structural dynamics need to be first incorporated using multiple experimentally and computationally obtained structures. The high-dimensional structural data may necessitate feature extraction methods to identify manageable representative structures for subsequent analyses. We have detailed state-of-the-art computational methods to (i) identify binding sites using data on sequence, shape, energy, etc., (ii) determine the allosteric nature of these binding sites based on structural ensembles, residue networks and correlated motions and (iii) identify small molecule binders through structure- and ligand-based virtual screening. To benefit future studies for targeting Rab allosteric sites, we herein detail a refined workflow comprising multiple available computational methods, which have been successfully used alone or in combinations. This workflow is also applicable for drug discovery efforts targeting other medically important proteins. Depending on the structural dynamics of proteins of interest, researchers can select suitable strategies for allosteric drug discovery and design, from the resources of computational methods and tools enlisted in the workflow.

Structural analysis of mycobacterial homoserine transacetylases central to methionine biosynthesis reveals druggable active site

Mycobacterium tuberculosis is the cause of the world’s most deadly infectious disease. Efforts are underway to target the methionine biosynthesis pathway, as it is not part of the host metabolism. The homoserine transacetylase MetX converts l-homoserine to O-acetyl-l-homoserine at the committed step of this pathway. In order to facilitate structure-based drug design, we determined the high-resolution crystal structures of three MetX proteins, including M. tuberculosis (MtMetX), Mycolicibacterium abscessus (MaMetX), and Mycolicibacterium hassiacum (MhMetX). A comparison of homoserine transacetylases from other bacterial and fungal species reveals a high degree of structural conservation amongst the enzymes. Utilizing homologous structures with bound cofactors, we analyzed the potential ligandability of MetX. The deep active-site tunnel surrounding the catalytic serine yielded many consensus clusters during mapping, suggesting that MtMetX is highly druggable.

The "rights" of precision drug development for Alzheimer's disease

There is a high rate of failure in Alzheimer's disease (AD) drug development with 99% of trials showing no drug-placebo difference. This low rate of success delays new treatments for patients and discourages investment in AD drug development. Studies across drug development programs in multiple disorders have identified important strategies for decreasing the risk and increasing the likelihood of success in drug development programs. These experiences provide guidance for the optimization of AD drug development. The "rights" of AD drug development include the right target, right drug, right biomarker, right participant, and right trial. The right target identifies the appropriate biologic process for an AD therapeutic intervention. The right drug must have well-understood pharmacokinetic and pharmacodynamic features, ability to penetrate the blood-brain barrier, efficacy demonstrated in animals, maximum tolerated dose established in phase I, and acceptable toxicity. The right biomarkers include participant selection biomarkers, target engagement biomarkers, biomarkers supportive of disease modification, and biomarkers for side effect monitoring. The right participant hinges on the identification of the phase of AD (preclinical, prodromal, dementia). Severity of disease and drug mechanism both have a role in defining the right participant. The right trial is a well-conducted trial with appropriate clinical and biomarker outcomes collected over an appropriate period of time, powered to detect a clinically meaningful drug-placebo difference, and anticipating variability introduced by globalization. We lack understanding of some critical aspects of disease biology and drug action that may affect the success of development programs even when the "rights" are adhered to. Attention to disciplined drug development will increase the likelihood of success, decrease the risks associated with AD drug development, enhance the ability to attract investment, and make it more likely that new therapies will become available to those with or vulnerable to the emergence of AD.

Computational and Experimental Druggability Assessment of Human DNA Glycosylases

Due to a polar or even charged binding interface, DNA-binding proteins are considered extraordinarily difficult targets for development of small-molecule ligands and only a handful of proteins have been targeted successfully to date. Recently, however, it has been shown that development of selective and efficient inhibitors of 8-oxoguanine DNA glycosylase is possible. Here, we describe the initial druggability assessment of DNA glycosylases in a computational setting and experimentally investigate several methods to target endonuclease VIII-like 1 (NEIL1) with small-molecule inhibitors. We find that DNA glycosylases exhibit good predicted druggability in both DNA-bound and -unbound states. Furthermore, we find catalytic sites to be highly flexible, allowing for a range of interactions and binding partners. One flexible catalytic site was rationalized for NEIL1 and further investigated experimentally using both a biochemical assay in the presence of DNA and a thermal shift assay in the absence of DNA.

Clinical Trials for Disease-Modifying Therapies in Alzheimer's Disease: A Primer, Lessons Learned, and a Blueprint for the Future

Alzheimer’s disease (AD) has no currently approved disease-modifying therapies (DMTs), and treatments to prevent, delay the onset, or slow the progression are urgently needed. A delay of 5 years if available by 2025 would decrease the total number of patients with AD by 50% in 2050. To meet the definition of DMT, an agent must produce an enduring change in the course of AD; clinical trials of DMTs have the goal of demonstrating this effect. AD drug discovery entails target identification followed by high throughput screening and lead optimization of drug-like compounds. Once an optimized agent is available and has been assessed for efficacy and toxicity in animals, it progresses through Phase I testing with healthy volunteers, Phase II learning trials to establish proof-of-mechanism and dose, and Phase III confirmatory trials to demonstrate efficacy and safety in larger populations. Phase III is followed by Food and Drug Administration review and, if appropriate, market access. Trial populations include cognitively normal at-risk participants in prevention trials, mildly impaired participants with biomarker evidence of AD in prodromal AD trials, and subjects with cognitive and functional impairment in AD dementia trials. Biomarkers are critical in trials of DMTs, assisting in participant characterization and diagnosis, target engagement and proof-of-pharmacology, demonstration of disease-modification, and monitoring side effects. Clinical trial designs include randomized, parallel group; delayed start; staggered withdrawal; and adaptive. Lessons learned from completed trials inform future trials and increase the likelihood of success.

Developing RNA aptamers for potential treatment of neurological diseases

AMPA receptor antagonists are drug candidates for potential treatment of a number of CNS diseases that involve excessive receptor activation. To date, small-molecule compounds are the dominating drug candidates in the field. However, lower potency, cross activity and poor water solubility are generally associated with these compounds. Here we show the potential of RNA-based antagonists or RNA aptamers as drug candidates and some strategies to discover these aptamers from a random sequence library (∼10¹⁴ sequences). As an alternative to small molecule compounds, our aptamers exhibit higher potency and selectivity toward AMPA receptors. Because aptamers are RNA molecules, they are naturally water soluble. We also discuss the major challenges of translating RNA aptamers as lead molecules into drugs/treatment options.