Going Small: Using Biophysical Screening to Implement Fragment Based Drug Discovery

Screening against biochemical targets with compact chemical fragments has developed a reputation as a successful early‐stage drug discovery approach, thanks to recent drug approvals. Having weak initial target affinities, fragments require the use of sensitive biophysical technologies (NMR, SPR, thermal shift, ITC, and X‐ray crystallography) to accommodate the practical limits of going smaller. Application of optimized fragment biophysical screening approaches now routinely allows for the rapid identification of fragments with high binding efficiencies. The aim of this chapter is to provide an introduction to fragment library selection and to discuss the suitability of screening approaches adapted for lower‐throughput biophysical techniques. A general description of metrics that are being used in the progression of fragment hits, the need for orthogonal assay testing, and guidance on potential pitfalls are included to assist scientists, considering initiating their own fragment discovery program

Design Principles for Fragment Libraries: Maximizing the Value of Learnings from Pharma Fragment-Based Drug Discovery (FBDD) Programs for Use in Academia

Fragment-based drug discovery (FBDD) is well suited for discovering both drug leads and chemical probes of protein function; it can cover broad swaths of chemical space and allows the use of creative chemistry. FBDD is widely implemented for lead discovery in industry but is sometimes. used less systematically in academia. Design principles and implementation approaches for fragment libraries are continually evolving, and the lack of up-to-date guidance may prevent more effective application of FBDD in academia. This Perspective explores many of the theoretical, practical, and strategic considerations that occur within FBDD programs, including the optimal size, complexity, physicochemical profile, and shape profile of fragments in FBDD libraries, as well as compound storage, evaluation; and screening technologies. This:compilation of industry experience in FBDD will hopefully be useful for those pursuing FBDD in academia

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery.

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds

NMR quality control of fragment libraries for screening

Fragment-based screening has evolved as a remarkable approach within the drug discovery process both in the industry and academia. Fragment screening has become a more structure-based approach to inhibitor development, but also towards development of pathway-specific clinical probes. However, it is often witnessed that the availability, immediate and long-term, of a high quality fragment-screening library is still beyond the reach of most academic laboratories. Within iNEXT (Infrastructure for NMR, EM and X-rays for Translational research), a EU-funded Horizon 2020 program, a collection of 782 fragments were assembled utilizing the concept of "poised fragments" with the aim to facilitate downstream synthesis of ligands with high affinity by fragment ligation. Herein, we describe the analytical procedure to assess the quality of this purchased and assembled fragment library by NMR spectroscopy. This quality assessment requires buffer solubility screening, comparison with LC/MS quality control and is supported by state-of-the-art software for high throughput data acquisition and on-the-fly data analysis. Results from the analysis of the library are presented as a prototype of fragment progression through the quality control process

Current perspectives in fragment based lead discovery (FBLD)

It is over 20 years since the first fragment-based discovery projects were disclosed. The methods are now mature for most ‘conventional’ targets in drug discovery such as enzymes (kinases and proteases) but there has also been growing success on more challenging targets, such as disruption of protein–protein interactions. The main application is to identify tractable chemical startpoints that non-covalently modulate the activity of a biological molecule. In this essay, we overview current practice in the methods and discuss how they have had an impact in lead discovery – generating a large number of fragment-derived compounds that are in clinical trials and two medicines treating patients. In addition, we discuss some of the more recent applications of the methods in chemical biology – providing chemical tools to investigate biological molecules, mechanisms and systems

Study of ligand-based virtual screening tools in computer-aided drug design

Virtual screening is a central technique in drug discovery today. Millions of molecules can be tested in silico with the aim to only select the most promising and test them experimentally. The topic of this thesis is ligand-based virtual screening tools which take existing active molecules as starting point for finding new drug candidates. One goal of this thesis was to build a model that gives the probability that two molecules are biologically similar as function of one or more chemical similarity scores. Another important goal was to evaluate how well different ligand-based virtual screening tools are able to distinguish active molecules from inactives. One more criterion set for the virtual screening tools was their applicability in scaffold-hopping, i.e. finding new active chemotypes. In the first part of the work, a link was defined between the abstract chemical similarity score given by a screening tool and the probability that the two molecules are biologically similar. These results help to decide objectively which virtual screening hits to test experimentally. The work also resulted in a new type of data fusion method when using two or more tools. In the second part, five ligand-based virtual screening tools were evaluated and their performance was found to be generally poor. Three reasons for this were proposed: false negatives in the benchmark sets, active molecules that do not share the binding mode, and activity cliffs. In the third part of the study, a novel visualization and quantification method is presented for evaluation of the scaffold-hopping ability of virtual screening tools.Siirretty Doriast

Clinical metagenomics.

Clinical metagenomic next-generation sequencing (mNGS), the comprehensive analysis of microbial and host genetic material (DNA and RNA) in samples from patients, is rapidly moving from research to clinical laboratories. This emerging approach is changing how physicians diagnose and treat infectious disease, with applications spanning a wide range of areas, including antimicrobial resistance, the microbiome, human host gene expression (transcriptomics) and oncology. Here, we focus on the challenges of implementing mNGS in the clinical laboratory and address potential solutions for maximizing its impact on patient care and public health

Targeting Trypanosoma brucei FPPS by Fragment-based drug discovery

Trypanosoma brucei (T. brucei) is the causative agent of the Human African Trypanosomiasis (HAT), which is a neglected disease with an endemic occurrence in 36 sub-Saharan African countries. The current standard of care suffers from low efficacy and severe side effects. Therefore, new drugs with better safety and efficacy profiles are urgently needed. Nitrogen-containing bisphosphonates, a current treatment for bone diseases, have been shown to block the growth of the T. brucei parasites by inhibiting farnesyl pyrophosphate synthase (FPPS); however, due to their particular pharmacokinetic properties they are not well suited for parasitic therapy. Recently, an additional allosteric site was discovered at the surface of human FPPS that, based on sequence analysis, is likely also present in T. brucei FPPS. The high unmet medical need combined with the discovery of a potential new target site prompted a fragment-based drug discovery approach to identify non-bisphosphonate binders on T. brucei FPPS, which is presented in this work. Fragment screening was performed by NMR and X-ray crystallography. To this end, a robust T. brucei FPPS crystallization system was established enabling high-throughput determination of crystal structures up to 1.67 Å resolution. Structural superimpositions revealed that the allosteric site found on human FPPS is in fact present in T. brucei FPPS. This observation enabled subsequent protein-observed NMR and crystal soaking experiments with established human FPPS binders resulting in three protein-ligand complex structures with bound fragments in the previously unknown allosteric site. For most of the tested binders, Kd by SPR was outside of experimental range for T. brucei FPPS and only for one fragment the Kd on T. brucei FPPS was determined three orders of magnitude higher than the IC50 value on human FPPS. Crystal structural analysis revealed a different binding mode on human and T. brucei FPPS with reduced protein-ligand interactions on T. brucei FPPS, which explains the significantly reduced binding affinity. Encouraged by the detection of first allosteric binders on T. brucei FPPS, fragment pools were screened by ligand-observed NMR and identified hits were followed-up by single compound ligand observed NMR and protein-observed NMR resulting in 25 validated fragment hits for T. brucei FPPS. Validated hits were followed-up by crystal soaking and co-crystallization experiments and seven protein-ligand complex structures were solved using PanDDA. Out of the seven fragments, four fragments were bound in the active site, one fragment was detected in the allosteric site that was identified as part of this thesis, and two fragments were bound in surface exposed binding sites. Notably, an active site bound fragment with a four atom long flexible linker adopted an orthogonal binding mode along αD when compared to the other three ligands. Sixteen fragment analogues of the elongated flexible active site fragment were tested by SAR using additional test compounds retrieved from catalogue and archive, and one crystal structure with a fragment analogue was solved and was surprisingly found in the allosteric site. In addition to the NMR fragment screen, an X-ray screen was performed at XChem (Diamond, UK) and at EMBL/ESRF (Grenoble, FR) resulting in seven protein-ligand structures. One fragment was positioned in the active site, three fragments in the allosteric site, two fragments in a cryptic site between helices αI and αH and one fragment at the opposite side of the allosteric site close to αG and αF. Fragment binding was further validated in protein-observed NMR. As fragments identified by such screening approaches typically exhibit low binding affinities usually in µM to mM range, structure-based fragment optimisation based on a fragment merging and growing approach was performed. In total, ten compounds were synthesised and subjected to protein observed NMR and X-ray structural analysis. Strikingly, a fragment merger based on T. brucei and T. cruzi active-site binders bound in a new binding site close to the SARM instead to the active site. Taken together, this work presents high-resolution structures of T. brucei FPPS and identified 19 compounds binding to seven different sites thereby paving the way for future studies aiming to identify high-affinity non-bisphosphonate inhibitors for T. brucei FPPS with pharmacokinetic properties that are suitable for parasitic indications

NOVEL ALGORITHMS AND TOOLS FOR LIGAND-BASED DRUG DESIGN

Computer-aided drug design (CADD) has become an indispensible component in modern drug discovery projects. The prediction of physicochemical properties and pharmacological properties of candidate compounds effectively increases the probability for drug candidates to pass latter phases of clinic trials. Ligand-based virtual screening exhibits advantages over structure-based drug design, in terms of its wide applicability and high computational efficiency. The established chemical repositories and reported bioassays form a gigantic knowledgebase to derive quantitative structure-activity relationship (QSAR) and structure-property relationship (QSPR). In addition, the rapid advance of machine learning techniques suggests new solutions for data-mining huge compound databases. In this thesis, a novel ligand classification algorithm, Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS), was reported for the prediction of diverse categorical pharmacological properties. LiCABEDS was successfully applied to model 5-HT1A ligand functionality, ligand selectivity of cannabinoid receptor subtypes, and blood-brain-barrier (BBB) passage. LiCABEDS was implemented and integrated with graphical user interface, data import/export, automated model training/ prediction, and project management. Besides, a non-linear ligand classifier was proposed, using a novel Topomer kernel function in support vector machine. With the emphasis on green high-performance computing, graphics processing units are alternative platforms for computationally expensive tasks. A novel GPU algorithm was designed and implemented in order to accelerate the calculation of chemical similarities with dense-format molecular fingerprints. Finally, a compound acquisition algorithm was reported to construct structurally diverse screening library in order to enhance hit rates in high-throughput screening