Genetic and proteomic approaches to identify cancer drug targets

Genome-wide CRISPR Screening to Identify Drivers of TGF-β-Induced Liver Fibrosis in Human Hepatic Stellate Cells

Liver fibrosis progression in chronic liver disease leads to cirrhosis, liver failure, or hepatocellular carcinoma and often ends in liver transplantation. Even with an increased understanding of liver fibrogenesis and many attempts to generate therapeutics specifically targeting fibrosis, there is no approved treatment for liver fibrosis. To further understand and characterize the driving mechanisms of liver fibrosis, we developed a high-throughput genome-wide CRISPR/Cas9 screening platform to identify hepatic stellate cell (HSC)-derived mediators of transforming growth factor (TGF)-β-induced liver fibrosis. The functional genomics phenotypic screening platform described here revealed the novel biology of TGF-β-induced fibrogenesis and potential drug targets for liver fibrosis.

Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology

Data-independent acquisition mass spectrometry (DIA-MS) is a next generation proteomic methodology that generates permanent digital proteome maps offering highly reproducible retrospective analysis of cellular and tissue specimens. The adoption of this technology has ushered a new wave of oncology studies across a wide range of applications including its use in molecular classification, oncogenic pathway analysis, drug and biomarker discovery and unravelling mechanisms of therapy response and resistance. In this review, we provide an overview of the experimental workflows commonly used in DIA-MS, including its current strengths and limitations versus conventional data-dependent acquisition mass spectrometry (DDA-MS). We further summarise a number of key studies to illustrate the power of this technology when applied to different facets of oncology. Finally we offer a perspective of the latest innovations in DIA-MS technology and machine learning-based algorithms necessary for driving the development of high-throughput, in-depth and reproducible proteomic assays that are compatible with clinical diagnostic workflows, which will ultimately enable the delivery of precision cancer medicine to achieve optimal patient outcomes.

Current Approaches to Drug Discovery for Chagas Disease: Methodological Advances

BACKGROUND

In recent years, there has been an improvement in the in vitro and in vivo methodology for the screening of anti-chagasic compounds. Millions of compounds can now have their activity evaluated (in large compound libraries) by means of high throughput in vitro screening assays.

OBJECTIVE

Current approaches to drug discovery for Chagas disease.

METHOD

This review article examines the contribution of these methodological advances in medicinal chemistry in the last four years, focusing on Trypanosoma cruzi infection, obtained from the PubMed, Web of Science, and Scopus databases.

RESULTS

Here, we have shown that the promise is increasing each year for more lead compounds for the development of a new drug against Chagas disease.

CONCLUSION

There is increased optimism among those working with the objective to find new drug candidates for optimal treatments against Chagas disease.

Therapeutic Targeting of Notch Signaling Pathway in Hematological Malignancies

The Notch pathway plays a key role in several processes, including stem-cell self-renewal, proliferation, and cell differentiation. Several studies identified recurrent mutations in hematological malignancies making Notch one of the most desirable targets in leukemia and lymphoma. The Notch signaling mediates resistance to therapy and controls cancer stem cells supporting the development of on-target therapeutic strategies to improve patients’ outcome. In this brief review, we outline the therapeutic potential of targeting Notch pathway in T-cell acute jlymphoblastic leukemia, chronic lymphocytic leukemia, and mantle cell lymphoma.

Strategies to Overcome Resistance Mechanisms in T-Cell Acute Lymphoblastic Leukemia

Chemoresistance is a major cause of recurrence and death from T-cell acute lymphoblastic leukemia (T-ALL), both in adult and pediatric patients. In the majority of cases, drug-resistant disease is treated by selecting a combination of other drugs, without understanding the molecular mechanisms by which malignant cells escape chemotherapeutic treatments, even though a more detailed genomic characterization and the identification of actionable disease targets may enable informed decision of new agents to improve patient outcomes. In this work, we describe pathways of resistance to common chemotherapeutic agents including glucocorticoids and review the resistance mechanisms to targeted therapy such as IL7R, PI3K-AKT-mTOR, NOTCH1, BRD4/MYC, Cyclin D3: CDK4/CDK6, BCL2 inhibitors, and selective inhibitors of nuclear export (SINE). Finally, to overcome the limitations of the current trial-and-error method, we summarize the experiences of anti-cancer drug sensitivity resistance profiling (DSRP) approaches as a rapid and relevant strategy to infer drug activity and provide functional information to assist clinical decision one patient at a time.

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

Natural Compounds as Anticancer Agents Targeting DNA Topoisomerases

DNA topoisomerases are important cellular enzymes found in almost all types of living cells (eukaryotic and prokaryotic). These enzymes are essential for various DNA metabolic processes e.g. replication, transcription, recombination, chromosomal decatenation etc. These enzymes are important molecular drug targets and inhibitors of these enzymes are widely used as effective anticancer and antibacterial drugs. However, topoisomerase inhibitors have some therapeutic limitations and they exert serious side effects during cancer chemotherapy. Thus, development of novel anticancer topoisomerase inhibitors is necessary for improving cancer chemotherapy. Nature serves as a repertoire of structurally and chemically diverse molecules and in the recent years many DNA topoisomerase inhibitors have been identified from natural sources. The present review discusses anticancer properties and therapeutic importance of eighteen recently identified natural topoisomerase inhibitors (from the year 2009 to 2015). Structural characteristics of these novel inhibitors provide backbones for designing and developing new anticancer drugs.

Complementary Approaches to Existing Target Based Drug Discovery for Identifying Novel Drug Targets

In the past decade, it was observed that the relationship between the emerging New Molecular Entities and the quantum of R&D investment has not been favorable. There might be numerous reasons but few studies stress the introduction of target based drug discovery approach as one of the factors. Although a number of drugs have been developed with an emphasis on a single protein target, yet identification of valid target is complex. The approach focuses on an in vitro single target, which overlooks the complexity of cell and makes process of validation drug targets uncertain. Thus, it is imperative to search for alternatives rather than looking at success stories of target-based drug discovery. It would be beneficial if the drugs were developed to target multiple components. New approaches like reverse engineering and translational research need to take into account both system and target-based approach. This review evaluates the strengths and limitations of known drug discovery approaches and proposes alternative approaches for increasing efficiency against treatment.

Use of big data in drug development for precision medicine

Drug development has been a costly and lengthy process with an extremely low success rate and lack of consideration of individual diversity in drug response and toxicity. Over the past decade, an alternative "big data" approach has been expanding at an unprecedented pace based on the development of electronic databases of chemical substances, disease gene/protein targets, functional readouts, and clinical information covering inter-individual genetic variations and toxicities. This paradigm shift has enabled systematic, high-throughput, and accelerated identification of novel drugs or repurposed indications of existing drugs for pathogenic molecular aberrations specifically present in each individual patient. The exploding interest from the information technology and direct-to-consumer genetic testing industries has been further facilitating the use of big data to achieve personalized Precision Medicine. Here we overview currently available resources and discuss future prospects.

Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines

High-content, image-based screens enable the identification of compounds that induce cellular responses similar to those of known drugs but through different chemical structures or targets. A central challenge in designing phenotypic screens is choosing suitable imaging biomarkers. Here we present a method for systematically identifying optimal reporter cell lines for annotating compound libraries (ORACLs), whose phenotypic profiles most accurately classify a training set of known drugs. We generate a library of fluorescently tagged reporter cell lines, and let analytical criteria determine which among them--the ORACL--best classifies compounds into multiple, diverse drug classes. We demonstrate that an ORACL can functionally annotate large compound libraries across diverse drug classes in a single-pass screen and confirm high prediction accuracy by means of orthogonal, secondary validation assays. Our approach will increase the efficiency, scale and accuracy of phenotypic screens by maximizing their discriminatory power.

Target identification of small molecules based on chemical biology approaches

Recently, a phenotypic approach-screens that assess the effects of compounds on cells, tissues, or whole organisms-has been reconsidered and reintroduced as a complementary strategy of a target-based approach for drug discovery. Although the finding of novel bioactive compounds from large chemical libraries has become routine, the identification of their molecular targets is still a time-consuming and difficult process, making this step rate-limiting in drug development. In the last decade, we and other researchers have amassed a large amount of phenotypic data through progress in omics research and advances in instrumentation. Accordingly, the profiling methodologies using these datasets expertly have emerged to identify and validate specific molecular targets of drug candidates, attaining some progress in current drug discovery (e.g., eribulin). In the case of a compound that shows an unprecedented phenotype likely by inhibiting a first-in-class target, however, such phenotypic profiling is invalid. Under the circumstances, a photo-crosslinking affinity approach should be beneficial. In this review, we describe and summarize recent progress in both affinity-based (direct) and phenotypic profiling (indirect) approaches for chemical biology target identification.

Venus Flytrap (Dionaea muscipula Solander ex Ellis) Contains Powerful Compounds that Prevent and Cure Cancer

Chemoprevention uses natural or synthetic molecules without toxic effects to prevent and/or block emergence and development of diseases including cancer. Many of these natural molecules modulate mitogenic signals involved in cell survival, apoptosis, cell cycle regulation, angiogenesis, or on processes involved in the development of metastases occur naturally, especially in fruits and vegetables bur also in non-comestible plants. Carnivorous plants including the Venus flytrap (Dionaea muscipula Solander ex Ellis) are much less investigated, but appear to contain a wealth of potent bioactive secondary metabolites. Aim of this review is to give insight into molecular mechanisms triggered by compounds isolated from these interesting plants with either therapeutic or chemopreventive potential.

[6]-Gingerol induces caspase 3 dependent apoptosis and autophagy in cancer cells: drug-DNA interaction and expression of certain signal genes in HeLa cells

[6]-Gingerol, a pharmacologically important bioactive component of ginger, has been reported to have anti-hyperglycemic, anti-cancer and anti-oxidative properties, but mechanisms through which these are achieved are largely unclear. The present study focuses on apoptosis and autophagy, two key events of anti-cancer activity, in HeLa cells treated with [6]-gingerol. The treated cells showed several morphological changes, including externalization of phosphatidyl serine, degradation of DNA and increase in TUNEL positivity. Furthermore, there was depolarization of mitochondrial membrane potential, providing evidence of mitochondria mediated apoptosis. The expression of caspase 3 and PARP was increased in cells exposed to [6]-gingerol. Circular dichroism study for testing drug-DNA interaction with both calf thymus and nuclear DNA as target revealed that the drug had potential to bind with the nuclear DNA and induce conformational changes of DNA. The over-expression of NFkβ, AKT and Bcl2 genes in cancer cells was down-regulated by [6]-gingerol treatment. On the other hand the expression levels of TNFα, Bax and cytochrome c were enhanced in [6]-gingerol treated cells. Thus, overall results suggest that [6]-gingerol has potential to bind with DNA and induce cell death by autophagy and caspase 3 mediated apoptosis.