Advances in Lead Generation

Site Identification and Next Choice Protocol for Hit-to-Lead Optimization

Time efficiency and cost savings are major challenges in drug discovery and development. In this process, the hit-to-lead stage is expected to improve efficiency because it primarily exploits the trial-and-error approach of medicinal chemists. This study proposes a site identification and next choice (SINCHO) protocol to improve the hit-to-lead efficiency. This protocol selects an anchor atom and growth site pair, which is desirable for a hit-to-lead strategy starting from a 3D complex structure. We developed and fine-tuned the protocol using a training data set and assessed it using a test data set of the preceding hit-to-lead strategy. The protocol was tested for experimentally determined structures and molecular dynamics (MD) ensembles. The protocol had a high prediction accuracy for applying MD ensembles, owing to the consideration of protein flexibility. The SINCHO protocol enables medicinal chemists to visualize and modify functional groups in a hit-to-lead manner.

Artificial Intelligence/Machine Learning-Driven Small Molecule Repurposing via Off-Target Prediction and Transcriptomics

The process of discovering small molecule drugs involves screening numerous compounds and optimizing the most promising ones, both in vitro and in vivo. However, approximately 90% of these optimized candidates fail during trials due to unexpected toxicity or insufficient efficacy. Current concepts with respect to drug-protein interactions suggest that each small molecule interacts with an average of 6-11 targets. This implies that approved drugs and even discontinued compounds could be repurposed by leveraging their interactions with unintended targets. Therefore, we developed a computational repurposing framework for small molecules, which combines artificial intelligence/machine learning (AI/ML)-based and chemical similarity-based target prediction methods with cross-species transcriptomics information. This repurposing methodology incorporates eight distinct target prediction methods, including three machine learning methods. By using multiple orthogonal methods for a "dataset" composed of 2766 FDA-approved drugs targeting multiple therapeutic target classes, we identified 27,371 off-target interactions involving 2013 protein targets (i.e., an average of around 10 interactions per drug). Relative to the drugs in the dataset, we identified 150,620 structurally similar compounds. The highest number of predicted interactions were for drugs targeting G protein-coupled receptors (GPCRs), enzymes, and kinases with 10,648, 4081, and 3678 interactions, respectively. Notably, 17,283 (63%) of the off-target interactions have been confirmed in vitro. Approximately 4000 interactions had an IC50 of <100 nM for 1105 FDA-approved drugs and 1661 interactions had an IC50 of <10 nM for 696 FDA-approved drugs. Together, the confirmation of numerous predicted interactions and the exploration of tissue-specific expression patterns in human and animal tissues offer insights into potential drug repurposing for new therapeutic applications.

Small-Molecule Lead-Finding Trends across the Roche and Genentech Research Organizations

The origin of small-molecule leads that were pursued across the independent research organizations Roche and Genentech from 2009 to 2020 is described. The identified chemical series are derived from a variety of lead-finding methods, which include public information, high-throughput screening (both full file and focused), fragment-based design, DNA-encoded library technology, use of legacy internal data, in-licensing, and de novo design (often structure-based). The translation of the lead series into in vivo tool compounds and development candidates is discussed as are the associated biological target classes and corresponding therapeutic areas. These analyses identify important trends regarding the various lead-finding approaches, which will likely impact their future application in the Roche and Genentech research groups. They also highlight commonalities and differences across the two independent research organizations. Several caveats associated with the employed data collection and analysis methodologies are included to enhance the interpretation of the presented information.

Late-stage C–H functionalization offers new opportunities in drug discovery

Over the past decade, the landscape of molecular synthesis has gained major impetus by the introduction of late-stage functionalization (LSF) methodologies. C-H functionalization approaches, particularly, set the stage for new retrosynthetic disconnections, while leading to improvements in resource economy. A variety of innovative techniques have been successfully applied to the C-H diversification of pharmaceuticals, and these key developments have enabled medicinal chemists to integrate LSF strategies in their drug discovery programmes. This Review highlights the significant advances achieved in the late-stage C-H functionalization of drugs and drug-like compounds, and showcases how the implementation of these modern strategies allows increased efficiency in the drug discovery process. Representative examples are examined and classified by mechanistic patterns involving directed or innate C-H functionalization, as well as emerging reaction manifolds, such as electrosynthesis and biocatalysis, among others. Structurally complex bioactive entities beyond small molecules are also covered, including diversification in the new modalities sphere. The challenges and limitations of current LSF methods are critically assessed, and avenues for future improvements of this rapidly expanding field are discussed. We, hereby, aim to provide a toolbox for chemists in academia as well as industrial practitioners, and introduce guiding principles for the application of LSF strategies to access new molecules of interest.

Artificial intelligence and the future of life sciences

Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials; mobile devices can also be used to improve aspects of both the diagnosis and treatment of diseases. In addition, AI can be used in the development of new drugs or for drug repurposing, in faster diagnosis and more efficient treatment for various diseases, as well as to identify data-driven hypotheses for scientists. In this review, we discuss how AI is starting to revolutionize the life sciences sector.

Identification of dihydrotanshinone I as an ERp57 inhibitor with anti-breast cancer properties via the UPR pathway

Salvia miltiorrhiza (Danshen) is a well-known traditional Chinese medicine for treating various diseases, such as breast cancer. However, knowledge regarding its mechanisms is scant. Herein, the active ingredient dihydrotanshinone I (DHT) in Salvia miltiorrhiza extract (SME), which binds ERp57 was identified and verified by an enzymatic solid-phase method combined with LC-MS/MS. DHT potentially inhibited ERp57 activity and suppressed ERp57 expression at both the RNA and protein levels. Molecular docking simulation indicated that DHT could form a hydrogen bond with catalytic site of ERp57. Moreover, ERp57 overexpression decreased DHT-induced cytotoxicity in MDA-MB-231 cells. Thereafter, the signaling pathway downstream of ERp57 was investigated by Western blot analysis. The mechanistic study revealed that DHT treatment resulted in activation of endoplasmic reticulum (ER) stress, the unfolded protein response (UPR), and cellular apoptosis. In conclusion, our data implied that DHT targeted ERp57 for inhibition and induced ER stress and UPR activation, which in turn triggered breast cancer cell apoptosis.

C-S Coupling of DNA-Conjugated Aryl Iodides for DNA-Encoded Chemical Library Synthesis

Thioethers have been widely found in biologically active compounds, including pharmaceuticals. In this report, a highly efficient approach to on-DNA construction of thioethers via Cu-promoted Ullmann cross-coupling between DNA-conjugated aryl iodides and thiols is developed. This methodology was demonstrated with medium to high yields, without obvious DNA damage. This reported reaction has strong potential for application in DNA-encoded chemical library synthesis.

Influence of the geometry of fluorescently labelled DNA constructs on fluorescence anisotropy assay

DNA-encoded chemical libraries (DECLs) are powerful tools for modern drug discovery. A DECL is a pooled mixture of small molecule compounds, each of which is tagged with a unique DNA sequence which functions as a barcode. After incubation with a drug target and washing to remove non-binders, the bound molecules are eluted and submitted for DNA sequencing to determine which molecules are binding the target. While the DECL technology itself is ultra-high throughput, the following re-synthesis of identified compounds for orthogonal validation experiments remains the bottleneck. Using existing DNA-small molecule conjugates directly for affinity measurements, as opposed to complete compound resynthesis, could accelerate the discovery process. To this end, we have tested various geometries of fluorescently-labelled DNA constructs for fluorescence anisotropy (FA) experiments. Minimizing the distance between the fluorescent moiety and ligand can maximize the correlation between ligand-protein interaction and corresponding change in fluorophore rotational freedom, thus leading to larger, easier to interpret changes in FA values. However, close proximity can also cause artifacts due to potentially promiscuous interactions between fluorophore and protein. By balancing these two opposite effects, we have identified applicable fluorescently labelled DNA constructs displaying either a single ligand or pairs of fragments for affinity measurement using a FA assay.

Novel phosphatidylinositol 4-kinases III beta (PI4KIIIβ) inhibitors discovered by virtual screening using free energy models

Herein, the LASSBio Chemical Library is presented as a valuable source of compounds for screening to identify hits suitable for subsequent hit-to-lead optimization stages. A feature of the LASSBio Chemical Library worth highlighting is the fact that it is a smart library designed by medicinal chemists with pharmacological activity as the main priority. The great majority of the compounds part of this library have shown in vivo activity in animal models, which is an indication that they possess overall favorable bioavailability properties and, hence, adequate pharmacokinetic profiles. This, in turn, is supported by the fact that approximately 85% of the compounds are compliant with Lipinski's rule of five and ca. 95% are compliant with Veber's rules, two important guidelines for oral bioavailability. In this work it is presented a virtual screening methodology combining a pharmacophore-based model and an empirical Gibbs free energy-based model for the ligand-protein interaction to explore the LASSBio Chemical Library as a source of new hits for the inhibition of the phosphatidylinositol 4-kinase IIIβ (PI4KIIIβ) enzyme, which is related to the development of viral infections (including enteroviruses, SARS coronavirus, and hepatitis C virus), cancers and neurological diseases. The approach resulted in the identification of two hits, LASSBio-1799 (7) and LASSBio-1814 (10), which inhibited the target enzyme with IC₅₀ values of 3.66 μM and IC₅₀ and 6.09 μM, respectively. This study also enabled the determination of the structural requirements for interactions with the active site of PI4KIIIβ, demonstrating the importance of both acceptor and donor hydrogen bonding groups for forming interactions with binding site residues Val598 and Lys549.

Augmenting Hit Identification by Virtual Screening Techniques in Small Molecule Drug Discovery

Two orthogonal approaches for hit identification in drug discovery are large-scale in vitro and in silico screening. In recent years, due to the emergence of new targets and a rapid increase in the size of the readily synthesizable chemical space, there is a growing emphasis on the integration of the two techniques to improve the hit finding efficiency. Here, we highlight three examples of drug discovery projects at Merck & Co., Inc., Kenilworth, NJ, USA in which different virtual screening (VS) techniques, each specifically tailored to leverage knowledge available for the target, were utilized to augment the selection of high-quality chemical matter for in vitro assays and to enhance the diversity and tractability of hits. Central to success is a fully integrated workflow combining in silico and experimental expertise at every stage of the hit identification process. We advocate that workflows encompassing VS as part of an integrated hit finding plan should be widely adopted to accelerate hit identification and foster cross-functional collaborations in modern drug discovery.

DNA-Encoded Library Screening as Core Platform Technology in Drug Discovery: Its Synthetic Method Development and Applications in DEL Synthesis

DNA-encoded library technology (DELT) was introduced to our medicinal chemistry society more than 20 years ago. The application of DELT in the development of clinical candidates has been actively reported in the literature recently. A few representative examples include RIP1K inhibitors for inflammatory diseases and sEH inhibitors for endothelial dysfunction or abnormal tissue repair, among many others. Here, the authors would like to recall the recent developments in on-DNA synthetic methodologies for DEL construction and to analyze recent examples in the literature of DELT-based drug development efforts pursued in both the academic and industrial sectors. With this perspective, we hope to provide a useful summary of recent DELT-based drug discovery research and to discuss the future scope of DELT in medicinal chemistry.

Medicines discovery for auditory disorders: Challenges for industry

Currently, no approved medicines are available for the prevention or treatment of hearing loss. Pharmaceutical industry productivity across all therapeutic indications has historically been disappointing, with a 90% chance of failure in delivering a marketed drug after entering clinical evaluation. To address these failings, initiatives have been applied in the three cornerstones of medicine discovery: target selection, clinical candidate selection, and clinical studies. These changes aimed to enable data-informed decisions on the translation of preclinical observations into a safe, clinically effective medicine by ensuring the best biological target is selected, the most appropriate chemical entity is advanced, and that the clinical studies enroll the correct patients. The specific underlying pathologies need to be known to allow appropriate patient selection, so improved diagnostics are required, as are methodologies for measuring in the inner ear target engagement, drug delivery and pharmacokinetics. The different therapeutic strategies of protecting hearing or preventing hearing loss versus restoring hearing are reviewed along with potential treatments for tinnitus. Examples of current investigational drugs are discussed to highlight key challenges in drug discovery and the learnings being applied to improve the probability of success of launching a marketed medicine.

Isocyanide Multicomponent Reactions on Solid-Phase-Coupled DNA Oligonucleotides for Encoded Library Synthesis

Isocyanide multicomponent reactions play a prominent role in drug discovery. This chemistry has hardly been investigated for compatibility with DNA-encoded combinatorial synthesis. The Ugi, Ugi-azide, and Groebke-Blackburn-Bienaymé reactions are well-tolerated by DNA on the solid phase and show a broad scope. However, an oxadiazole-forming variant of the Ugi reaction caused DNA depurination, requiring a more stable hexathymidine DNA for encoded library synthesis. Cheminformatic analysis revealed that isocyanide multicomponent-reaction-based encoded libraries cover a diverse chemical space.

Using Physicochemical Measurements to Influence Better Compound Design

During the past decade, the physicochemical quality of molecules under investigation at all stages of the drug discovery process has come under particular scrutiny. The issues associated with excessive lipophilicity and poor solubility in particular are many and varied, ranging from poor outcomes in screening campaigns to promiscuity, limited and/or poorly predictable pharmacokinetic exposure, and, ultimately, greater chances of clinical failure. In this review, contemporary methods to secure key measurements are described along with their relevance to understanding the behavior of molecules in environments pertinent to pharmacological activity. Together, the various measurements contribute to predictive models of both the physicochemical properties themselves and the outcomes they influence.