Oxetanes inDrug Discovery Campaigns

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

Applications of oxetanes in drug discovery and medicinal chemistry

Aqueous solubility is an important physicochemical parameter for any potential drug candidate, and high-throughput kinetic assays are frequently used in drug discovery to give an estimate of a compound's aqueous solubility. However, the aqueous solubility data from an equilibrium (thermodynamic) shake-flask technique is considered more relevant, but is slower and more labor intensive to generate. A highly automated aqueous equilibrium solubility shake-flask technique is described and validated on a set of 15 marketed drugs, whose aqueous solubilities cover four orders of magnitude. The assay uses a Tecan Freedom Evo 200 liquid handling robot (Tecan Group Ltd., Männerdorf, Switzerland) with integrated appliances for the transportation, decapping and recapping, and centrifugation of sample tubes. These bespoke automation solutions help overcome the labor intensive steps associated with performing conventional, gold standard, aqueous equilibrium solubility shake-flask measurements, enabling the assay to be used as a primary-wave drug discovery screen.

Reviewed by: Medicating Modern America: Prescription Drugs in History Susan L. Speaker Andrea Tone and Elizabeth Siegel Watkins, eds. Medicating Modern America: Prescription Drugs in History. New York: New York University Press, 2007. vi + 262 pp. Ill. $70.00 (cloth, ISBN-10: 0-8147-8300-7, ISBN-13: 978-0-8147-8300-9), $22.00 (paperbound, ISBN-10: 0-8147-8301-5, ISBN-13: 978-0-8147-8301-6). Few topics in the history of medicine present as many facets for study as drugs do. Our medicines can—and do—function not just as curative agents but as scientific and economic commodities, cultural icons, and instruments of hope or despair. Because of their central role in medicine, they shape research agendas and funding, and sometimes the very definition of diseases and the shape of medical practice. And they provoke endless questions about commercial, professional, and personal power and accountability. The eight essays in Medicating Modern America provide rich, multilayered histories of a representative sample of modern prescription drugs: antibiotics, mood stabilizers, stimulants, oral contraceptives, hormone replacements, statins, tranquilizers, and Viagra. [End Page 970] The editors have chosen to group the essays according to the era of the drugs' origins rather than by drug class or targeted disease. Thus the antibiotics, mood stabilizers, and hormone replacements developed before 1945 compose the first section. Oral contraceptives, minor tranquilizers, and the amphetamine drugs used to treat juvenile hyperactivity represent postwar drug innovation and marketing. And two relative newcomers, the cholesterol-lowering statins and the impotence drug Viagra, complete the volume. This excellent strategy allows a fuller appreciation of how the uses and expectations of different drugs change with time and experience. The contributors have provided fresh perspectives on older drugs and welcome new information about more recent drugs, in all cases giving readers thorough and nuanced accounts of the drugs' "life cycles." A short review can only note some highlights: Robert Bud's insightful essay examines how professional and cultural presentations of antibiotics have "re-branded" these drugs in the current era of emerging resistant diseases. David Healy explores the emergence of the term "mood stabilizer" to describe drugs used to control manic symptoms (e.g., lithium) and their use in conjunction with the recent vogue of "bipolar disorder." Elizabeth Watkins's chapter on hormone replacement therapy draws on her earlier work but also on a cache of 4,500 responses to a 1986 Saturday Evening Post reader survey, to demonstrate the wide spectrum of attitudes and practices regarding hormone replacement therapy among menopausal women. Suzanne White Junod examines the challenges faced by physicians and regulators during the 1970s as they sought to effectively communicate information about the risks of blood clots with oral contraceptive use. Their efforts, she notes, exposed consumers for the first time to the idea that drugs were not merely "safe" or "unsafe" but had benefits offset by certain risks. Ilina Singh's chapter on the drug treatment of Attention Deficit-Hyperactivity Disorder (ADHD) is especially welcome: despite increased prescribing of the stimulant drugs methylphenidate (Ritalin) and amphetamine for ADHD in recent decades, little has been written about their history, mostly because the drug industry's primary sources are often difficult to access. Singh traces the history through drug advertisements instead, showing that such ads played on older associations between "problem boys" (the "classic" disruptive ADHD children) and their problematic mothers. Andrea Tone's excellent "Tranquilizers on Trial" follows the minor tranquilizers (e.g., Miltown and Valium) from their discovery in the 1950s through their rapid diffusion into medical practice, to their eventual political and cultural discrediting, deftly situating the popular and controversial drugs within the medical, commercial, and cultural shifts of the 1950s, '60s, and '70s. In the final section, Jeremy Greene uses the story of the first cholesterol-lowering drug, Merck's lovastatin (Mevacor) to examine the ways in which drug discovery, clinical trials, industry marketing, and public health campaigns have interacted since 1980 to redefine the boundaries of "normal" and "abnormal" cholesterol levels. In her history of Viagra, Jennifer Fishman places its development squarely within a shifting diagnostic context in which the psychological disorder of "impotence" became the medical problem called "erectile dysfunction." [End Page 971...

Die Entwicklung robuster und zuverlässiger Methoden für den Aufbau von C(sp2)−C(sp3)‐Bindungen ist entscheidend, um eine Vielzahl strukturell diverser Substanzen in der Wirkstoffforschung und ‐entwicklung zugänglich zu machen. Obwohl bedeutende Fortschritte auf diesem Gebiet durch den Einsatz der Metallaphotoredox‐Chemie erzielt wurden, nutzen viele dieser Methoden nach wie vor Photokatalysatoren auf Edelmetallbasis aufgrund ihrer effizienten Redoxprozesse und gut anpassbaren Eigenschaften. Aufgrund der hohen Kosten, Ressourcenknappheit und oftmals erheblichen Toxizität dieser Metalle, ist es von großem Interesse, die Suche nach geeigneten Ersatz voranzutreiben. In dieser Publikation zeigen wir den Einsatz des kommerziell erhältlichen, heterogenen Halbleiters graphitisches Kohlenstoffnitrid (gCN) als Photokatalysator in Kombination mit Nickelkatalyse für die Kreuzkupplung von Arylhalogenid‐ und Carbonsäure‐Derivaten. Wie in vorherigen Berichten gezeigt, kann gCN effektiv an Einelektronenübertragungen (SET) und Energietransfer‐(EnT) Prozessen zur Bildung von C−X‐Bindungen teilnehmen. Dieses Manuskript beinhaltet neue Strategien, um die bislang auf C−O Bindungsknüpfung beschränkten Methoden mittels carbonsäurehaltigen Bausteinen um C−C Bindungsknüpfungen zu erweitern. Es wird gezeigt, dass eine Vielzahl an Arylhalogeniden als auch Carbonsäuren für diese Transformation genutzt werden kann. Im Weiteren wird das Recycling des Photokatalysators demonstriert und der Mechanismus der Reaktion untersucht.

Mock, MG, Hirsch, KR, Blue, MNM, Trexler, ET, Roelofs, EJ, and Smith-Ryan, AE. Postexercise ingestion of low or high molecular weight glucose polymer solution does not improve cycle performance in female athletes. J Strength Cond Res 35(1): 124-131, 2021-The current study sought to evaluate the effects of postexercise ingestion of a high molecular weight (HMW) glucose polymer solution compared with an isocaloric low molecular weight (LMW) solution or placebo (PLA) on subsequent cycling performance in female athletes. In a randomized, double-blind, placebo-controlled, cross-over design, 10 competitive female cyclists (Mean ± SD; Age = 25.7 ± 5.0 years; V̇o2peak = 49.7 ± 4.3 ml·kg-1·min-1) completed 3 testing sessions separated by 7-10 days. Visits consisted of a ride-to-exhaustion (RTE) at 75% V̇o2peak, followed by immediate consumption of 700 ml containing either: 1.2 g·kg-1 LMW (maltodextrin/dextrose/fructose); 1.2 g·kg-1 HMW (Vitargo); or 0.066 g·kg-1 PLA (noncaloric flavoring). After 2 hours of rest, subjects performed a 15-minute time trial (TT). Respiratory exchange ratio (RER) was assessed via indirect calorimetry during exercise. Total body water (TBW) was measured using bioelectrical impedance to assess fluid balance. When covaried for estrogen, there was no treatment effect on distance (km; p = 0.632) or power output (watts; p = 0.974) during the 15-minute TT. Respiratory exchange ratio was not significantly different during the LMW and HWM TTs (p > 0.999), but both were significantly higher than PLA (p = 0.039, p = 0.001, respectively). Changes in total body water pre-exercise to postexercise were not significantly different between trials (p = 0.777). Despite benefits of HMW on cycling performance previously reported in males, current results demonstrate no ergogenic effect of HMW or LMW in females. Sex differences in substrate utilization may account for the discrepancy, and further research involving performance nutrition for female athletes is merited.

Here, we describe a method to detect the oxidation of the pool of low molecular weight thiols (LMWT) in the infective stage of Trypanosoma brucei brucei in situ and non-invasively. The redox reporter cell line was generated by transfecting the parasites with a DNA construct coding for a redox-sensitive green fluorescent protein fused to human glutaredoxin 1 (hGrx1-roGFP2). The reporter gene is expressed in a tetracycline-inducible manner in the parasite's cytosol. roGFP2 displays a ratiometric and reversible change in fluorescence emission at 510nm when excited at 405 and 488nm, which is proportional to the changes in the ratio of oxidized vs. reduced LMWT trypanothione and glutathione. The role of hGrx1 is to catalyze a rapid equilibration of the LMWT pool with roGFP2 redox state, thereby allowing a biosensor response within seconds. The dynamic response of the biosensor enables the monitoring of cellular events in response to drugs or other stimuli in real time. The assay was adapted to a 96-well plate format for flow cytometry-based analysis. The fluorescent readout can be intensiometric or ratiometric, depending on the flow cytometer features, and the use of calibration controls is recommended for quantitative analysis. This bioassay can be applied to study fundamental questions of trypanosomatids' redox biology, as go/no-go criteria in drug discovery campaigns, and to investigate drug mode of action.

The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.

Modern drug discovery relies on combinatorial screening campaigns to find drug molecules targeting specific disease-associated proteins. The success of such campaigns often relies on functional and structural information of the selected therapeutic target, only achievable once its purification is mastered. With the aim of bypassing the protein purification process to gain insights on the druggability, ligand binding, and/or characterization of protein-protein interactions, herein, we describe the Extract2Chip method. This approach builds on the immobilization of site-specific biotinylated proteins of interest, directly from cellular extracts, on avidin-coated sensor chips to allow for the characterization of molecular interactions via surface plasmon resonance (SPR). The developed method was initially validated using Cyclophilin D (CypD) and subsequently applied to other drug discovery projects in which the targets of interest were difficult to express, purify, and crystallize. Extract2Chip was successfully applied to the characterization of Yes-associated protein (YAP): Transcriptional enhancer factor TEF (TEAD1) protein-protein interaction inhibitors, in the validation of a ternary complex assembly composed of Dyskerin pseudouridine synthase 1 (DKC1) and RuvBL1/RuvBL2, and in the establishment of a fast-screening platform to select the most suitable NUAK family SNF1-like kinase 2 (NUAK2) surrogate for binding and structural studies. The described method paves the way for a potential revival of the many drug discovery campaigns that have failed to deliver due to the lack of suitable and sufficient protein supply.

Positioning High-Throughput CETSA in Early Drug Discovery through Screening against B-Raf and PARP1

A key element of studies that utilise ion mobility mass spectrometry (IM-MS) under native electrospray conditions for the analysis of protein-ligand binding is the maintenance of the native conformation of a protein during the removal of bulk solvent. Ruotolo and co-workers have demonstrated that the binding and subsequent dissociation of the anionic component of inorganic salts stabilise native protein conformations in the gas phase. In this study, we investigated the effect that organic acid fragments identified from a fragment-based drug discovery (FBDD) campaign might have on the gas-phase stability of carbonic anhydrase II (CA II). We utilised native IM-MS to monitor changes in the conformation of CA II in the absence and presence of four acidic fragments. By performing a series of collision-induced unfolding (CIU) experiments we determined the effect of fragment binding on the gas-phase stability of CA II. Binding and dissociation of acidic fragments result in increased gas-phase stability of CA II. CFU experiments revealed that the native-like compact gas-phase conformation of the protein is stable with higher degree of pre-activation when bound to a series of acidic fragments. Importantly, although acetate was present in high concentrations, the stabilising effect was not observed without the addition of the acidic fragments. Binding and subsequent dissociation of acidic fragments from CA II significantly delayed CIU in a manner which is probably analogous to the effect of inorganic anions. Furthermore, we saw a slightly altered stabilising effect between the different fragments investigated in this study. This suggests that the prevention of CIU by organic acids may be tuneable to specific properties of a bound ligand. These observations may open avenues to exploit IM-MS as a screening platform in FBDD.

Computational chemistry methods can significantly reduce experimental costs in early stages of a drug development project by filtering out unsuitable candidates and discovering new chemical matter. Molecular alignment is a key pre-requisite for 3D similarity evaluation between compounds and pharmacophore elucidation. Relying on the hypothesis that the variation in maximal achievable binding affinity for an optimized drug-like molecule is largely due to desolvation, we explore herein a novel small molecule 3D alignment strategy that exploits the partitioning of molecular hydrophobicity into atomic contributions in conjunction with information about the distribution of hydrogen-bond donor/acceptor groups in each compound. A brief description of the method, as implemented in the software package PharmScreen, is presented. The computational procedure is calibrated by using a dataset of 402 molecules pertaining to 14 distinct targets taken from the literature and validated against the CCDC AstraZeneca test set of 121 experimentally derived molecular overlays. The results confirm the suitability of MST based-hydrophobic parameters for generating molecular overlays with correct predictions obtained for 100%, 93%, and 55% of the molecules classified into easy, moderate and hard sets, respectively. The potential of this tool in a drug discovery campaign is then evaluated in a retrospective study with the aim to evaluate the correlations between activities and similarity score of a series of sigma-1 receptor ligands. The results confirm the suitability of the tool for Drug Discovery purposes finding the 67% of the most active ligands (≤10 nM) in Q1 of the ranking and the most active compound in position five.

The caspase family of cysteine proteases are highly sought‐after drug targets owing to their essential roles in apoptosis, proliferation, and inflammation pathways. High‐throughput screening efforts to discover inhibitors have gained little traction. Fragment‐based screening has emerged as a powerful approach for the discovery of innovative drug leads. This method has become a central facet of drug discovery campaigns in the pharmaceutical industry and academia. A fragment‐based drug discovery campaign against human caspase‐7 resulted in the discovery of a novel series of allosteric inhibitors. An X‐ray crystal structure of caspase‐7 bound to a fragment hit and a thorough kinetic characterization of a zymogenic form of the enzyme were used to investigate the allosteric mechanism of inhibition. This work further advances our understanding of the mechanisms of allosteric control of this class of pharmaceutically relevant enzymes, and provides a new path forward for drug discovery efforts.

Virtual screening (VS) methods have been shown to increase success rates in many drug discovery campaigns, when they complement experimental approaches, such as high-throughput screening methods or classical medicinal chemistry approaches. Nevertheless, predictive capability of VS is not yet optimal, mainly due to limitations in the underlying physical principles describing drug binding phenomena. One approach that can improve VS methods is the aid of machine learning methods. When enough experimental data are available to train such methods, predictive capability can considerably increase. We show in this research work how a multi-objective evolutionary search strategy for feature selection, which can provide with small and accurate decision trees that can be very easily understood by chemists, can drastically increase the applicability and predictive ability of these techniques and therefore aid considerable in the drug discovery problem. With the proposed methodology, we find classification models with accuracy between 0.9934 and 1.00 and area under ROC between 0.96 and 1.00 evaluated in full training sets, and accuracy between 0.9849 and 0.9940 and area under ROC between 0.89 and 0.93 evaluated with tenfold cross-validation over 30 iterations, while substantially reducing the model size.

The holistic integration of virtual screening in drug discovery