Expedited Discovery Research Articles

An expedited screening platform for the discovery of anti-ageing compounds in vitro and in vivo

BackgroundRestraining or slowing ageing hallmarks at the cellular level have been proposed as a route to increased organismal lifespan and healthspan. Consequently, there is great interest in anti-ageing drug discovery. However, this currently requires laborious and lengthy longevity analysis. Here, we present a novel screening readout for the expedited discovery of compounds that restrain ageing of cell populations in vitro and enable extension of in vivo lifespan.MethodsUsing Illumina methylation arrays, we monitored DNA methylation changes accompanying long-term passaging of adult primary human cells in culture. This enabled us to develop, test, and validate the CellPopAge Clock, an epigenetic clock with underlying algorithm, unique among existing epigenetic clocks for its design to detect anti-ageing compounds in vitro. Additionally, we measured markers of senescence and performed longevity experiments in vivo in Drosophila, to further validate our approach to discover novel anti-ageing compounds. Finally, we bench mark our epigenetic clock with other available epigenetic clocks to consolidate its usefulness and specialisation for primary cells in culture.ResultsWe developed a novel epigenetic clock, the CellPopAge Clock, to accurately monitor the age of a population of adult human primary cells. We find that the CellPopAge Clock can detect decelerated passage-based ageing of human primary cells treated with rapamycin or trametinib, well-established longevity drugs. We then utilise the CellPopAge Clock as a screening tool for the identification of compounds which decelerate ageing of cell populations, uncovering novel anti-ageing drugs, torin2 and dactolisib (BEZ-235). We demonstrate that delayed epigenetic ageing in human primary cells treated with anti-ageing compounds is accompanied by a reduction in senescence and ageing biomarkers. Finally, we extend our screening platform in vivo by taking advantage of a specially formulated holidic medium for increased drug bioavailability in Drosophila. We show that the novel anti-ageing drugs, torin2 and dactolisib (BEZ-235), increase longevity in vivo.ConclusionsOur method expands the scope of CpG methylation profiling to accurately and rapidly detecting anti-ageing potential of drugs using human cells in vitro, and in vivo, providing a novel accelerated discovery platform to test sought after anti-ageing compounds and geroprotectors.

An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations

Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.

Advances in screening hyperthermic nanomedicines in 3D tumor models.

Hyperthermic nanomedicines are particularly relevant for tackling human cancer, providing a valuable alternative to conventional therapeutics. The early-stage preclinical performance evaluation of such anti-cancer treatments is conventionally performed in flat 2D cell cultures that do not mimic the volumetric heat transfer occurring in human tumors. Recently, improvements in bioengineered 3D in vitro models have unlocked the opportunity to recapitulate major tumor microenvironment hallmarks and generate highly informative readouts that can contribute to accelerating the discovery and validation of efficient hyperthermic treatments. Leveraging on this, herein we aim to showcase the potential of engineered physiomimetic 3D tumor models for evaluating the preclinical efficacy of hyperthermic nanomedicines, featuring the main advantages and design considerations under diverse testing scenarios. The most recent applications of 3D tumor models for screening photo- and/or magnetic nanomedicines will be discussed, either as standalone systems or in combinatorial approaches with other anti-cancer therapeutics. We envision that breakthroughs toward developing multi-functional 3D platforms for hyperthermia onset and follow-up will contribute to a more expedited discovery of top-performing hyperthermic therapies in a preclinical setting before their in vivo screening.

Cell-specific imputation of drug connectivity mapping with incomplete data.

Drug repositioning allows expedited discovery of new applications for existing compounds, but re-screening vast compound libraries is often prohibitively expensive. "Connectivity mapping" is a process that links drugs to diseases by identifying compounds whose impact on expression in a collection of cells reverses the disease's impact on expression in disease-relevant tissues. The LINCS project has expanded the universe of compounds and cells for which data are available, but even with this effort, many clinically useful combinations are missing. To evaluate the possibility of repurposing drugs despite missing data, we compared collaborative filtering using either neighborhood-based or SVD imputation methods to two naive approaches via cross-validation. Methods were evaluated for their ability to predict drug connectivity despite missing data. Predictions improved when cell type was taken into account. Neighborhood collaborative filtering was the most successful method, with the best improvements in non-immortalized primary cells. We also explored which classes of compounds are most and least reliant on cell type for accurate imputation. We conclude that even for cells in which drug responses have not been fully characterized, it is possible to identify unassayed drugs that reverse in those cells the expression signatures observed in disease.

Label-free affinity screening, design and synthesis of inhibitors targeting the Mycobacterium tuberculosis L-alanine dehydrogenase.

The ability of Mycobacterium tuberculosis (Mtb) to persist in its host may enable an evolutionary advantage for drug resistant variants to emerge. A potential strategy to prevent persistence and gain drug efficacy is to directly target the activity of enzymes that are crucial for persistence. We present a method for expedited discovery and structure-based design of lead compounds by targeting the hypoxia-associated enzyme L-alanine dehydrogenase (AlaDH). Biochemical and structural analyses of AlaDH confirmed binding of nucleoside derivatives and showed a site adjacent to the nucleoside binding pocket that can confer specificity to putative inhibitors. Using a combination of dye-ligand affinity chromatography, enzyme kinetics and protein crystallographic studies, we show the development and validation of drug prototypes. Crystal structures of AlaDH-inhibitor complexes with variations at the N6 position of the adenyl-moiety of the inhibitor provide insight into the molecular basis for the specificity of these compounds. We describe a drug-designing pipeline that aims to block Mtb to proliferate upon re-oxygenation by specifically blocking NAD accessibility to AlaDH. The collective approach to drug discovery was further evaluated through in silico analyses providing additional insight into an efficient drug development strategy that can be further assessed with the incorporation of in vivo studies.

Hardening Effects in Superhard Transition-Metal Borides

ConspectusMechanical hardness is a physical property used to gauge the applications of materials in the manufacturing and machining industries. Because of their high hardness and wear resistance, superhard materials (Vickers hardness, Hv ≥ 40 GPa) are commonly used as cutting tools and abrasives. Although diamond is the hardest known material used for industrial applications, its synthesis requires both high pressure and high temperature. Interest in the field of superhard materials research has led to the search for alternatives with high hardness and thermal stability at low cost. The discovery of novel ultraincompressible, superhard materials has largely developed through trial and error along two paths. In one approach, researchers combine light elements, such as boron, carbon, nitrogen, and oxygen, often at high pressure, to replicate the highly directional, dense, covalent bonds of diamond. In the second approach, these light elements (B, C, N, and O) are combined with highly incompressible, electron-rich transition metals to form dense covalently bonded networks at ambient pressure.In this Account, we highlight our progress in developing superhard transition-metal borides through solid solution effects and grain boundary strengthening. We begin with a review of the factors that contribute to a material’s hardness and guide our design parameters of high electron density and high covalent bond density in the search for new materials. In subsequent sections, we examine various metal boride systems with increasing bond covalency and structural complexity, from metal-rich mono- and diborides to boron-rich tetra- and dodecaborides. The metal borides discussed in this Account are formed at ambient pressure using high-temperature solid-state techniques such as arc melting and molten flux synthesis. By characterizing these materials through both Vickers hardness testing and high-pressure experiments, we gain insight into the coupled effects of bonding and grain morphology on mechanical properties. Finally, we provide an outlook into the expedited discovery and accessible compositions for future materials. We hope that the materials and methods discussed in this Account offer new opportunities for the design and synthesis of the next generation of superhard materials for industrial applications.

Therapeutic Update on Huntington's Disease: Symptomatic Treatments and Emerging Disease-Modifying Therapies.

Huntington's disease (HD) is a monogenic neurodegenerative disorder that presents with progressive motor, behavior, and cognitive symptoms leading to early disability and mortality. HD is caused by an expanded CAG repeats in exon 1 of the huntingtin (HTT) gene. The corresponding genetic test allows a clinical, definite diagnosis in life and the identification of a fully penetrant mutation carrier in a premanifest stage. In addition to the development of symptomatic treatments that attempt to address unmet care needs such as apathy, irritability, and cognition, novel therapies that target pathways specific to HD biology are being developed with the intent of slowing disease progression. Among these approaches, HTT protein lowering therapies hold great promise. There are currently active programs using antisense oligonucleotides (ASOs), RNA interference, small-molecule splicing modulators, and zinc-finger protein transcription factor. Except for ASOs and RNA interference approaches, the remaining therapeutic strategies are at a preclinical stage of development. While the current therapeutic landscape in HD may bring an unparalleled change in the lives of people with HD and their families with the first-ever disease-modifying therapy, the evaluation of these therapies requires novel tools that enable a more efficient and expedited discovery and evaluative process. Examples are biomarkers targeting the HTT protein to measure target engagement or disease progression and rating scales more sensitive to the earliest clinical changes. These tools will be instrumental in the next phase of disease-modifying clinical trials in HD likely to target the phenoconversion period of the disease, including the prodromal HD stage.

A machine learning based approach for phononic crystal property discovery

Phononic crystals are artificially structured materials that can possess special vibrational properties that enable advanced manipulations of sound and heat transport. These special properties originate from the formation of a bandgap that prevents the excitation of entire frequency ranges in the phononic band diagram. Unfortunately, identifying phononic crystals with useful bandgaps is a problematic process because not all phononic crystals have bandgaps. Predicting if a phononic crystal structure has a bandgap, and if so, the gap's center frequency and width is a computationally expensive process. Herein, we explore machine learning as a rapid screening tool for expedited discovery of phononic bandgap presence, center frequency, and width. We test three different machine learning algorithms (logistic/linear regression, artificial neural network, and random forests) and show that random forests performs the best. For example, we show that a random phononic crystal selection has only a 17% probability of having a bandgap, whereas after incorporating rapid screening with the random forests model, this probability increases to 89%. When predicting the bandgap center frequency and width, this model achieves coefficient of determinations of 0.66 and 0.85, respectively. If the model has a priori knowledge that a bandgap exists, the coefficients of determination for center and width improve to 0.97 and 0.85, respectively. We show that most of the model's performance gains are achieved for training datasets as small as ∼5000 samples. Training the model with just 500 samples led to reduced performance but still yielded algorithms with predictive values.

The Importance of Being Dismissive: The Efficiency Role of Pleading Stage Evaluation of Shareholder Litigation

It has been claimed that the risk/reward dynamics of shareholder litigation have encouraged quick settlements with substantial attorneys’ fee awards but no payment to shareholders, regardless of the merits of the case. Fee-shifting charter and bylaw provisions may be too blunt a tool to control agency costs associated with excessive shareholder litigation, and are in any event now prohibited by Delaware statute. We claim, however, that active judicial supervision of public company shareholder litigation at an early stage reduces the costs of frivolous litigation to shareholders by separating meritorious from unmeritorious litigation before the full costs of discovery are incurred. Using procedures and doctrines that have not previously been catalogued and appreciated as a coherent set of interrelated dynamics, the Delaware Court of Chancery has relied on the motion to dismiss as the primary procedural vehicle for accomplishing that early stage triage. Such early stage analysis depends upon consideration of essentially undisputed facts, and upon the availability of such facts to the plaintiff shareholder through sources that compensate for the problem of asymmetric access to relevant information. The motion to dismiss in representative shareholder litigation has thus come to resemble, and substitute for, the motion for summary judgment. The Delaware courts’ atypical demand for, and unusual willingness to consider, extensive facts in resolving motions to dismiss encourage defendants to supply relevant information voluntarily, on a cost efficient basis that avoids largely unlimited discovery. Where time constraints preclude disposition via a motion to dismiss, the motion for expedited discovery must necessarily come to serve the same efficiency promoting functions as the motion to dismiss, and the Court of Chancery has come to apply essentially the same level of substantive factual review of the merits encountered in resolving motions to dismiss. The result is a system in which cases are dismissed or settle at the motion to dismiss stage: from 2011 through 2014, for example, there were only four public company shareholder class or derivative suits in which the Court of Chancery resolved the case after trial. With the likely concentration of deal litigation in the Delaware courts resulting from increasingly prevalent exclusive forum charter and bylaw provisions, the motion to dismiss and the motion to expedite discovery are likely to become even more important in promoting the efficient conduct of shareholder class and derivative litigation involving public companies.

Analysis of Secreted Proteins as an in vitro Model for Discovery of Liver Toxicity Markers

Despite the wealth of sequence data and new technologies that can scan large portions of the transcriptome or proteome in a single experiment, attempts to identify human biomarkers of toxicity have been met with limited success. We have adapted an in vitro model system to identify proteins secreted by a human hepatoma-derived cell line (HepG2/C3A) in response to toxicant exposure. Using quantitative proteomics, we can find alterations in the abundance of proteins at the source of damage-liver cells-that are likely to be present in blood samples of exposed animals. In a proof of concept experiment, conditioned medium from cells exposed to ethanol was subjected to quantitative mass spectral analysis after abundant proteins were immunodepleted. Eighty-seven proteins were identified with almost half changing in abundance. Some of these were only identified in the highest treatment condition and presumably result from the release of intracellular proteins into the medium when the cell membrane is disrupted upon cell death. However, the majority of the identified proteins reflect known consequences of ethanol exposure or alcoholism. The analysis of proteins found in conditioned medium after exposure to toxicants appears to be a useful system for the expedited discovery of potential human biomarkers.

Gene prioritization in Type 2 Diabetes using domain interactions and network analysis

BackgroundIdentification of disease genes for Type 2 Diabetes (T2D) by traditional methods has yielded limited success. Based on our previous observation that T2D may result from disturbed protein-protein interactions affected through disrupting modular domain interactions, here we have designed an approach to rank the candidates in the T2D linked genomic regions as plausible disease genes.ResultsOur approach integrates Weight value (Wv) method followed by prioritization using clustering coefficients derived from domain interaction network. Wv for each candidate is calculated based on the assumption that disease genes might be functionally related, mainly facilitated by interactions among domains of the interacting proteins. The benchmarking using a test dataset comprising of both known T2D genes and non-T2D genes revealed that Wv method had a sensitivity and specificity of 0.74 and 0.96 respectively with 9 fold enrichment. The candidate genes having a Wv > 0.5 were called High Weight Elements (HWEs). Further, we ranked HWEs by using the network property-the clustering coefficient (Ci). Each HWE with a Ci < 0.015 was prioritized as plausible disease candidates (HWEc) as previous studies indicate that disease genes tend to avoid dense clustering (with an average Ci of 0.015). This method further prioritized the identified disease genes with a sensitivity of 0.32 and a specificity of 0.98 and enriched the candidate list by 6.8 fold. Thus, from the dataset of 4052 positional candidates the method ranked 435 to be most likely disease candidates. The gene ontology sharing for the candidates showed higher representation of metabolic and signaling processes. The approach also captured genes with unknown functions which were characterized by network motif analysis.ConclusionsPrioritization of positional candidates is essential for cost-effective and an expedited discovery of disease genes. Here, we demonstrate a novel approach for disease candidate prioritization from numerous loci linked to T2D.

Towards a data publishing framework for primary biodiversity data: challenges and potentials for the biodiversity informatics community

BackgroundCurrently primary scientific data, especially that dealing with biodiversity, is neither easily discoverable nor accessible. Amongst several impediments, one is a lack of professional recognition of scientific data publishing efforts. A possible solution is establishment of a 'Data Publishing Framework' which would encourage and recognise investments and efforts by institutions and individuals towards management, and publishing of primary scientific data potentially on a par with recognitions received for scholarly publications.DiscussionThis paper reviews the state-of-the-art of primary biodiversity data publishing, and conceptualises a 'Data Publishing Framework' that would help incentivise efforts and investments by institutions and individuals in facilitating free and open access to biodiversity data. It further postulates the institutionalisation of a 'Data Usage Index (DUI)', that would attribute due recognition to multiple players in the data collection/creation, management and publishing cycle.ConclusionWe believe that institutionalisation of such a 'Data Publishing Framework' that offers socio-cultural, legal, technical, economic and policy environment conducive for data publishing will facilitate expedited discovery and mobilisation of an exponential increase in quantity of 'fit-for-use' primary biodiversity data, much of which is currently invisible.

Discovery of an Exceptionally Potent and Selective Class of Fatty Acid Amide Hydrolase Inhibitors Enlisting Proteome‐Wide Selectivity Screening: Concurrent Optimization of Enzyme Inhibitor Potency and Selectivity.

AbstractFor Abstract see ChemInform Abstract in Full Text.

Discovery of an exceptionally potent and selective class of fatty acid amide hydrolase inhibitors enlisting proteome-wide selectivity screening: concurrent optimization of enzyme inhibitor potency and selectivity

The concurrent implementation of a proteome-wide serine hydrolase selectivity screen with traditional efforts to optimize fatty acid amide hydrolase (FAAH) inhibition potency led to the expedited discovery of a new class of exceptionally potent ( K i < 300 pM) and unusually selective (>100-fold selective) inhibitors. The iterative inhibitor design and evaluation with assistance of the selectivity screen served to differentiate otherwise indistinguishable inhibitors permitting the simultaneous optimization of potency and selectivity. Significantly, the simultaneous assessment of all potential competitive enzymes with the selectivity screen does not require the use of expressed or purified enzymes or a competitive substrate, no modification of the inhibitors is required, and the relative potency for competitive enzymes can be quantified (IC 50’s) including those that lack known substrates or function.