The parallel and combinatorial synthesis and screening in drug discovery

Reliable compound screening is fundamental to drug discovery, yet the process remains undermined by lack of robust risk controls of false compound selection or omission in current methods. To address these challenges, we introduced conformal selection as an enhanced approach to optimize the compound screening process with balanced risks and benefits. Leveraging conformal inference, our approach constructs p-values for each candidate molecule to quantify statistical evidence for selection. The final selection of molecules is determined by comparing these p-values against thresholds derived from multiple testing principles. Our approach offers rigorous control over the false discovery/omission rate, ensuring validity independent of data set size and requiring minimal assumptions. By avoiding the estimation of prediction errors required in previous approaches, our method achieves higher power, thereby improving the ability to identify promising candidates. We validate these advantages through numerical simulations on real-world data sets.

Different strategies for the discovery of novel leads interacting with therapeutically relevant targets are thoroughly presented and discussed, using also three recent examples. Emphasis is given to approaches which do not require extensive resources and budgets, but rather prove how cleverness and creativity can provide active compounds in drug discovery.

Webster’s New Collegiate Dictionary defines promise as ‘... a right to expect or to claim the performance or forbearance of a specified act; ground for hope, expectation or assurance of eventual success ...’. Combinatorial chemistry has certainly generated very high expectations in the drug discovery community, but has the technique of combinatorial chemistry, and the practitioners of the technique, really delivered on that promise? The real promise of combinatorial chemistry should be defined in terms of a drug discovery and not a preclinical or, for that matter, a clinical candidate. Quite clearly, combinatorial methodology has yet to deliver on that promise, since no drugs have yet to emerge from clinical trials. However, for the purposes of this chapter, the editors of this series hope to document the continued advance of drug candidates generated by combinatorial methods through the clinic and, ultimately, to the market place. That is, the promise fulfilled. At this stage in the development of combinatorial methods, this chapter represents more of a place holder than a chapter of substance. In a sense, this chapter is as much an unfulfilled promise as the technique it reports upon, since there is really very little accessible clinical data to report. Therefore, we will outline the seemingly ever-expanding list of practicing combinatorial chemistry companies, report anecdotal information concerning pre-clinical discoveries and, most significantly, summarize the progress of those candidates actually in clinical development. In some respects the pre-clinical information would be very useful, but, not too surprisingly, this is very difficult information to document and to verify. Hopefully, in the not too distant future, there will be a detailed drug discovery story, with combinatorial chemistry as the centerpiece, which will appear in this chapter.KeywordsDrug DiscoveryStructural ClassCombinatorial MethodCombinatorial ChemistryLead StructureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Chapter 26. Discovery and Identification of Lead Compounds from Combinatorial Mixtures

In this paper, the slow evolution of combinatorial chemistry from its dawn in 1980’s to today’s oil field product development is discussed. Combinatorial chemistry comprises chemical synthetic methods that make it possible to prepare a vast number of compounds in a single process. These compound libraries can be made as mixtures, sets of individual compounds or chemical structures generated by computer software. This phenomenon was first invented by Arpad Furka (Lorand University, Budapest) in 1982. He described the principle of it, the combinatorial synthesis and a deconvolution procedure. The methodology was first used in drug discovery using a wide range of linear or wide range of macrocyclic chemical molecules: peptides, non-peptide oligomers, peptidomimetics, small-molecules, and natural product-like organic molecules. However, handling vast amounts of data and extremely small chemical recovery were a very difficult endeavor. To avoid this problem and help to refine the size of the chemical libraries, various software programs were utilized. This was achieved by utilizing a tool known as Design of Experiment (DoE). In this paper, the high throughput product screening to identify corrosion inhibitors was performed by utilizing critical micelle concentration (CMC). CMC was used to differentiate performance of libraries of chemical blends. Combinatorial synthesis (or blends) and combinatorial screening were performed by utilizing robotics methodologies. The corrosion inhibitor formulations predicted by DoE were built out by using combinatorial chemical methods and the arrays of chemical formulations were screened by utilizing high throughput robotics, using CMC as the selection guide. To validate the concept, several known corrosion inhibitor formulas were selected to optimize their efficacy. Each formula contained several active ingredients and a solvent package. These raw materials were blended in random but in a control, manner using combinatorial methodologies. After formulation of a vast array of formulation by using Design Expert solvent package. These raw materials were blended in random but in a control, manner using combinatorial methodologies. After formulation of a vast array of formulation by using Design Expert (DE) software, the products were screened for by CMC using automated surface tension workstation. Several formulations with lower CMC than the reference products were selected. The selected corrosion inhibitor formulations were identified and blended in larger scales. The efficacy of these products was tested by classical laboratory testing methods such as rotating cylinder electrode (RCE) and rotating cage autoclave (RCA) to determine their performance as anti-corrosion agents. These tests were performed against the original reference corrosion inhibitor. The testing indicated that several corrosion inhibitor formulations outperform the original blend thus validating the proof of concept.

A combinatorial materials science method for screening potential elemental additions to mitigate tin whisker formation on Pb-free electronics is presented. This combinatorial method is demonstrated using compositional gradient libraries deposited on glass substrates. Fabrication and heat treatment of combinatorial libraries is discussed. The effectiveness of this high-throughput screening method becomes evident from the analysis of two ternary systems (W-Cr-Ni and Mo-Al-Co) deposited over a tin matrix on glass substrates. Scanning electron microscopy revealed that tin whiskers tend to nucleate and grow on samples with no compressive stresses associated with the formation of intermetallic layers. Additionally, a nickel layer between the glass and tin was found to hinder the formation of whiskers; however, the traditional argument of Ni as a diffusion barrier between Sn-Cu not longer explains the observed behavior. This combinatorial method provides a rapid assessment tool to identify potential elemental mixtures to mitigate tin whiskers while using a simple, cost effective experimental approach.

Making Space for Anti-Infective Drug Discovery

Combinatorial synthesis and screening for discovery of electrocatalysts has received increasing attention, particularly for energy-related technologies. High-throughput discovery strategies typically employ a fast, reliable initial screening technique that is able to identify active catalyst composition regions. Traditional electrochemical characterization via current-voltage measurements is inherently throughput-limited, as such measurements are most readily performed by serial screening. Parallel screening methods can yield much higher throughput and generally require the use of an indirect measurement of catalytic activity. In a water-splitting reaction, the change of local pH or the presence of oxygen and hydrogen in the solution can be utilized for parallel screening of active electrocatalysts. Previously reported techniques for measuring these signals typically function in a narrow pH range and are not suitable for both strong acidic and basic environments. A simple approach to screen the electrocatalytic activities by imaging the oxygen and hydrogen bubbles produced by the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) is reported here. A custom built electrochemical cell was employed to record the bubble evolution during the screening, where the testing materials were subject to desired electrochemical potentials. The transient of the bubble intensity obtained from the screening was quantitatively analyzed to yield a bubble figure of merit (FOM) that represents the reaction rate. Active catalysts in a pseudoternary material library, (Ni-Fe-Co)Ox, which contains 231 unique compositions, were identified in less than one minute using the bubble screening method. An independent, serial screening method on the same material library exhibited excellent agreement with the parallel bubble screening. This general approach is highly parallel and is independent of solution pH.

The development of new superconductors (SCs) is important not only for their application but also for their potential to contribute to the progress of solid-state physics and materials science. In this article, we describe an approach to efficiently search for new SCs utilizing a combinatorial chemistry method. The method consists of preparation of bulk samples containing numerous various compounds and an effective screening method to detect new SCs in the bulk sample. We will show the effectiveness of the bulk sample preparation using high-pressure synthesis method and examples of new SCs discovered by this combinatorial method.

A combinatorial approach to the optimization of metastable multicomponent hard coatings

The one-bead one-compound (OBOC) combinatorial library method enables the rapid generation and screening of millions of discrete chemical compounds on beads. Most of the OBOC screening methods require the library compounds to remain tethered to the bead during screening process. Methods have also been developed to release library compounds from immobilized beads for in situ solution phase or "lawn" assays. However, this latter approach, while extremely powerful, is severely limited by the lack of suitable solid supports for such assays. Here, we report on the development of a novel hydrogel TentaGel shell-core (HTSC) bead in which hydrogel is grafted onto the polystyrene-based TentaGel (TG) bead as an outer shell (5-80 mum thick) via free radical surface-initiated polymerization. This novel shell-core bilayer resin enables the preparation of encoded OBOC combinatorial small molecule libraries, such that the library compounds reside on the highly hydrophilic outer layer and the coding tags reside in the polystyrene-based TG core. Using fluorescein as a model small molecule compound, we have demonstrated that fluorescein molecules that have been linked covalently to the hydrogel shell via a disulfide bond could readily diffuse out of the hydrogel layer into the bead surrounding after reduction with dithiothreitol. In contrast, under identical condition, the released fluorescein molecules remained bound to unmodified TG bead. We have prepared an encoded OBOC small molecule library on the novel shell-core beads and demonstrated that the beads can be readily decoded.

Chapter 14 - Mechanism-based high-throughput screening for novel anticancer drug discovery

Oncostatin M (OSM) is a pleiotropic, interleukin-6 family inflammatory cytokine that plays an important role in inflammatory diseases, including inflammatory bowel disease, rheumatoid arthritis, and cancer progression and metastasis. Recently, elevated OSM levels have been found in the serum of COVID-19 patients in intensive care units. Multiple anti-OSM therapeutics have been investigated, but to date no OSM small molecule inhibitors are clinically available. To pursue a high-throughput screening and structure-based drug discovery strategy to design a small molecule inhibitor of OSM, milligram quantities of highly pure, bioactive OSM are required. Here, we developed a reliable protocol to produce highly pure unlabeled and isotope enriched OSM from E. coli for biochemical and NMR studies. High yields (ca. 10 mg/L culture) were obtained in rich and minimal defined media cultures. Purified OSM was characterized by mass spectrometry and circular dichroism. The bioactivity was confirmed by induction of OSM/OSM receptor signaling through STAT3 phosphorylation in human breast cancer cells. Optimized buffer conditions yielded 1H, 15N HSQC NMR spectra with intense, well-dispersed peaks. Titration of 15N OSM with a small molecule inhibitor showed chemical shift perturbations for several key residues with a binding affinity of 12.2 ± 3.9 μM. These results demonstrate the value of bioactive recombinant human OSM for NMR-based small molecule screening.

Identifying the novel and potential starting lead compounds remains major challenge in drug discovery industry. In 90’s, high-throughput screening is a common practice in early stage of a project; screening of large number compounds to identify potential starting lead compounds which can modulate target of their interest. Although high-throughput screening has some remarkable successes in identifying potential drug leads, still HTVS is not fruitful as expected, due to the costly nature of chemicals, assays and also depends on the chemical space of the input library [1-4]. Over the years, tremendous research has carried out towards the designing of virtual libraries and virtual screening to identifying the potential starting leads, to reduce the cost of High throughput Screening and to improve the success rate. In current issue of “Current Topics in Medicinal Chemistry (CTMC)”, we focused on the recent trends in library design, Denovo studies, structure and ligand-based virtual screening approaches towards the medicinal chemistry and drug discovery topics and entitled the special topic with “Recent Trends in Library Design and Virtual Screening in Medicinal Chemistry and Drug Discovery”. Articles in this special issue have been contributed by experts from many parts of the world. In the first article, Drs. Rahul and Se Won Park discussed about discovery of a tuberculosis drug: bedaquiline, its anti-tuberculosis effects, mode of action and also discussed about computer-aided drug design approach to predict the binding mode for bedaquiline. In the next article, Drs. Prema Latha & Thomas discussed about successful application of structure-based drug design of Zanamivir (Relenza™) and oseltamivir (Tamiflu ® ), antiviral drugs. Also, discussed about application of computer-aided methods in identifying leads against influenza targets. Next article contributed by Drs. Harikishore & Yoon discusses about recent developments in denovo drug design and also explained successful application of the ligand and receptor based de novo drug design approaches. In the next article, Drs. Rodolpho & Carolina, summarizes the recent developments in virtual screening strategies and also highlights the recent achievements and as well as challenges. Also, discussed recent example of successful application for the identification of novel hit compounds for Trypanosoma cruzi sterol 14 α-demethylase (CYP51). In another article, Drs. Dimitar, Girinath, & Mati has covered a variety of studies have demonstrated the potential of machine- learning methods for predicting compounds as potential drug candidates. Also, provided an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. In next article, Drs. Evanthia & Zoe Cournia has covered an overview to the principles and applications of Structure-based Virtual Screening (VS) approaches and discussed on recent trends in library design, and as well as discuss limitations of the method.

Hypothesis Testing in High-Throughput Screening for Drug Discovery