The Discovery of Medicines from Plants: A Current Biological Perspective1

The Discovery of Medicines from Plants: A Current Biological Perspective. The last 50 years have seen tremendous innovations in the process of discovering novel bioactive compounds from plants. Every stage of the natural products discovery and development pipeline has seen major advances, so as a result, today it is possible to evaluate large numbers of plant extracts efficiently and in more effective ways against a wider array of disease targets. Despite all of these technological advances and numerous large-scale discovery efforts, the number of new drugs developed as natural products from plants to reach the market has been surprisingly low, very different from the continuing productive discovery work from microbes. The innovations in the discovery process are reviewed, possible explanations for low success of recent discovery efforts are explored, and an effort is made to estimate the future potential of plants as a discovery resource. It may be that the low rate of discovery during this developmental period of natural products discovery is a consequence of technological limitations of the discovery process rather than a lack of interesting compounds in plants, and that even when necessary assumptions are taken into account, conservative estimates would indicate great potential for the discovery of new drugs from plants.     

Mathematical algorithm for discovering states of expression from direct genetic comparison by microarrays

Highly specific direct genome-scale expression discovery from two biological samples facilitates functional discovery of molecular systems. Here, expression data from cDNA arrays are ranked and curve-fitted. The algorithm uses filters based on the derivatives (slopes) of the curve fits. The rules are set to (i) filter the largest number of artifactual ratios from same-to-same datasets and (ii) maximize discovery from direct comparisons of different samples. The unsupervised discovery is optimized without lowering specificity. The false discovery rates are significantly lower than other methods. The discovered states of genetic expression facilitate functional discovery and are validated by real-time RT–PCR. Better quality improves sensitivity.     

Fishing for new antimicrobials

The discovery of antibiotics and other antimicrobial agents in the 1930s is arguably the most significant therapeutic advance in medical history. Penicillin and the sulfa drugs touched off the search for and discovery of countless derivative compounds and several new antibiotic classes. However, the pace of discovery has slowed down, and there is growing appreciation that much of the low-lying fruit accessible to traditional methods of antimicrobial discovery has been harvested. Combating emerging drug-resistant strains of infectious agents may require the adoption of fresh approaches to drug target validation, small-molecule discovery and safety assessment. The recent development of several infectious disease models in zebrafish raises the possibility of a new paradigm in antimicrobial discovery.     

Industrializing AI/ML during the end-to-end drug discovery process

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)-based drug discovery companies have attempted to build data-driven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.     

Patterns of species discovery in the Western Ghats,a megadiversity hot spot in India

Even since Linnaeus, naturalists and taxonomists have been systematically describing species new to science. Besides indicating gaps in taxonomic effort, understanding the temporal patterns of species discovery could help in identifying drivers that determine discovery. In this study we report the patterns of discovery of eight taxa — birds, butterflies, frogs, tiger beetles, grasses, asters, ferns and orchids — in the Western Ghats, a megadiversity centre in India. Our results indicate that the discovery curves for birds and butterflies have been saturated while those for frogs and grasses continue to increase. Within each taxon, the major drivers of discovery were commonness of the species and their size. The average years taken for discovery across taxa were directly related to the per cent endemicity and species richness of the taxa. We discuss the trajectories of discovery with respect to rarity or endemicity of the species and life history features, and the implications these might have for strategizing the discovery process in India.     

Testing Methods: The Evaluation of Discovery Operations in Evolutionary Biology

Scientific discovery requires both abstract, theoretically defined concepts and discovery operations formed by sets of rules that permit the empirical detection of instances of those concepts. In this paper, I examine the ontological status of discovery operations and the tests employed to evaluate them in evolutionary biology. Attention is drawn to the distinction between nomothetic (universal, predictive) and ideographic (historical, retrodictive) discovery operations, and between complementary and exclusive discovery operations. Three types of tests of discovery operations are commonly employed in evolutionary biology. Theoretical tests aim to show that a discovery operation is inconsistent with accepted, well-corroborated, empirical theories. Empirical tests evaluate the performance of competing discovery operations in terms of their results when applied to the same empirical data sets. Philosophical tests aim to show that an operation is inconsistent with logical and epistemological principles. Appropriately designed theoretical and philosophical tests of ideographic discovery operations may be scientifically valid. Empirical tests, however, are incapable of evaluating the scientific merits of competing discovery operations. Nonetheless, empirical comparisons (not tests ) of competing discovery operations may provide insight into the ways discovery operations may be misleading and therefore may play an important role in stimulating critical debate and eventually establishing a scientifically optimal operation. In practice, theoretical and philosophical tests are often combined to test competing discovery operations as rigorously as possible.     

Fast food in ant communities: how competing species find resources

An understanding of foraging behavior is crucial to understanding higher level community dynamics; in particular, there is a lack of information about how different species discover food resources. We examined the effect of forager number and forager discovery capacity on food discovery in two disparate temperate ant communities, located in Texas and Arizona. We defined forager discovery capacity as the per capita rate of resource discovery, or how quickly individual ants arrived at resources. In general, resources were discovered more quickly when more foragers were present; this was true both within communities, where species identity was ignored, as well as within species. This pattern suggests that resource discovery is a matter of random processes, with ants essentially bumping into resources at a rate mediated by their abundance. In contrast, species that were better discoverers, as defined by the proportion of resources discovered first, did not have higher numbers of mean foragers. Instead, both mean forager number and mean forager discovery capacity determined discovery success. The Texas species used both forager number and capacity, whereas the Arizona species used only forager capacity. There was a negative correlation between a species’ prevalence in the environment and the discovery capacity of its foragers, suggesting that a given species cannot exploit both high numbers and high discovery capacity as a strategy. These results highlight that while forager number is crucial to determining time to discovery at the community level and within species, individual forager characteristics influence the outcome of exploitative competition in ant communities.     

Drug discovery in academia

Drug discovery and development is generally done in the commercial rather than the academic realm. Drug discovery involves target discovery and validation, lead identification by high-throughput screening, and lead optimization by medicinal chemistry. Follow-up preclinical evaluation includes analysis in animal models of compound efficacy and pharmacology (ADME: administration, distribution, metabolism, elimination) and studies of toxicology, specificity, and drug interactions. Notwithstanding the high-cost, labor-intensive, and non-hypothesis-driven aspects of drug discovery, the academic setting has a unique and expanding niche in this important area of investigation. For example, academic drug discovery can focus on targets of limited commercial value, such as third-world and rare diseases, and on the development of research reagents such as high-affinity inhibitors for pharmacological "gene knockout" in animal models ("chemical genetics"). This review describes the practical aspects of the preclinical drug discovery process for academic investigators. The discovery of small molecule inhibitors and activators of the cystic fibrosis transmembrane conductance regulator is presented as an example of an academic drug discovery program that has yielded new compounds for physiology research and clinical development. high-throughput screening; drug development; pharmacology; fluorescence; cystic fibrosis transmembrane conductance regulator     

The effects of discovery learning in a lower-division biology course

This study investigated discovery learning pedagogy and its effects on students' achievement and attitudes toward instruction in a lower-division biology course, entitled Structure and Function of Organisms. Instruction was primarily lecture-based but included four discovery learning activities. Results indicate that students had greater achievement on content learned through discovery methods than lecture-based instruction. Findings regarding students' attitudes toward discovery-based instruction suggest that students enjoyed active, discovery-based problems, believed that discovery helped them gain an understanding of the material and helped them to develop skills that could be used in other courses. The study presented here shows that a moderate amount of discovery learning used in combination with traditional methods of instruction may be an effective means for promoting students' achievement.     

Information management for entomology screening

The successful introduction of genetically modified (GM) crops has created a revolutionary transformation of the agricultural industry with GM crops currently planted on some 130 million acres. Historically, agricultural companies were involved in the discovery of conventional insecticide chemistries in processes that were very similar to those in the pharmaceutical industry. With the introduction of GM technologies, the process has radically changed. The new process is best described as the pharmaceutical equivalent of a very large-scale limited clinical trial conducted at the discovery stage. For example, unlike pharmaceutical screens, GM crop discovery screens frequently involve live insects challenged with complex protein containing soups. Furthermore, the nature of the potential avenues for discovery mandates that assay models also support protein engineering, proteomics, and genomics efforts. The range in sample demand ranges from ultra-low to high throughput. Finally, regulatory and good business stewardship demands that data integrity and tractability is in place, from discovery through varietal introduction, to manage the information generated in support of a commercialized GM plant variety. The authors describe their discovery workflow and detail how they have customized a commercially available database software package to achieve an optimum configuration for entomology screening.     

Testing paradigm for prediction of development-limiting barriers and human drug toxicity

The financial investment grows exponentially as a new chemical entity advances through each stage of discovery and development. The opportunity exists for the modern toxicologist to significantly impact expenditures by the early prediction of potential toxicity/side effect barriers to development by aggressive evaluation of development-limiting liabilities early in drug discovery. Improved efficiency in pharmaceutical research and development lies both in leveraging "best in class" technology and integration with pharmacologic activities during hit-to-lead and early lead optimization stages. To meet this challenge, a discovery assay by stage (DABS) paradigm should be adopted. The DABS clearly delineates to discovery project teams the timing and type of assay required for advancement of compounds to each subsequent level of discovery and development. An integrative core pathology function unifying Drug Safety Evaluation, Molecular Technologies and Clinical Research groups that effectively spans all phases of drug discovery and development is encouraged to drive the DABS. The ultimate goal of such improved efficiency being the accurate prediction of toxicity and side effects that would occur in development before commitment of the large prerequisite resource. Good justification of this approach is that every reduction of development attrition by 10% results in an estimated increase in net present value by $100 million.     

Natural products drug discovery: accelerating the clinical candidate development using reverse pharmacology approaches

The pharmaceutical industry is facing serious challenges as the drug discovery process is becoming extremely expensive, riskier and critically inefficient. A significant shift from single to multi targeted drugs especially for polygenic syndromes is being witnessed. Strategic options based on natural product drug discovery, ethnopharmacology and traditional medicines are re-emerging to offer good base as an attractive discovery engine. Approaches based on reverse pharmacology may offer efficient development platforms for herbal formulations. Relevant case studies from India and other countries where such approaches have expedited the drug discovery and development process by reducing time and economizing investments with better safety are discussed.     

Bioinformatics approaches for new drug discovery: a review

Prolonged antibiotic therapy for the bacterial infections has resulted in high levels of antibiotic resistance. Initially, bacteria are susceptible to the antibiotics, but can gradually develop resistance. Treating such drug-resistant bacteria remains difficult or even impossible. Hence, there is a need to develop effective drugs against bacterial pathogens. The drug discovery process is time-consuming, expensive and laborious. The traditionally available drug discovery process initiates with the identification of target as well as the most promising drug molecule, followed by the optimization of this, in-vitro, in-vivo and in pre-clinical studies to decide whether the compound has the potential to be developed as a drug molecule. Drug discovery, drug development and commercialization are complicated processes. To overcome some of these problems, there are many computational tools available for new drug discovery, which could be cost effective and less time-consuming. In-silico approaches can reduce the number of potential compounds from hundreds of thousands to the tens of thousands which could be studied for drug discovery and this results in savings of time, money and human resources. Our review is on the various computational methods employed in new drug discovery processes.     

The oldest complete skeleton of an Australopithecus in Africa (StW 573)

Although 78 years have elapsed since the discovery at Taung of the Australopithecus africanus, and despite intensive fieldwork in East Africa which resulted in 32 years of non-stop excavation at Sterkfontein, there has not been a discovery to date of a reasonably intact skull and associated skeleton of an ape-man. The following report is an account of an extraordinary series of events that led to the discovery of a complete skeleton on an Australopithecus, and a preliminary assessment of the significance of the fossil, which is still 5 years after its discovery largely embedded in the Member 2 breccia of the Sterkfontein Caves near Krugersdorp, South Africa.     

Quantitative trait Loci analysis using the false discovery rate

False discovery rate control has become an essential tool in any study that has a very large multiplicity problem. False discovery rate-controlling procedures have also been found to be very effective in QTL analysis, ensuring reproducible results with few falsely discovered linkages and offering increased power to discover QTL, although their acceptance has been slower than in microarray analysis, for example. The reason is partly because the methodological aspects of applying the false discovery rate to QTL mapping are not well developed. Our aim in this work is to lay a solid foundation for the use of the false discovery rate in QTL mapping. We review the false discovery rate criterion, the appropriate interpretation of the FDR, and alternative formulations of the FDR that appeared in the statistical and genetics literature. We discuss important features of the FDR approach, some stemming from new developments in FDR theory and methodology, which deem it especially useful in linkage analysis. We review false discovery rate-controlling procedures--the BH, the resampling procedure, and the adaptive two-stage procedure-and discuss the validity of these procedures in single- and multiple-trait QTL mapping. Finally we argue that the control of the false discovery rate has an important role in suggesting, indicating the significance of, and confirming QTL and present guidelines for its use.     

Will genomics revolutionize antimicrobial drug discovery?

Utilizing genome sequence data from bacterial and fungal pathogens for the discovery of new antimicrobial agents has received considerable attention, both practical and critical, from the pharmaceutical and biotechnological communities. Although no new drugs derived from genomics-based discovery have been reported to be in a development pipeline, the utilization of genomics has revolutionized many aspects of drug discovery. The application, utility, opportunity, and challenges afforded by many of these new approaches are discussed.     

Long-term potentiation and memory

The discovery of long-term potentiation (LTP) transformed research on the neurobiology of learning and memory. This did not happen overnight, but the discovery of an experimentally demonstrable phenomenon reflecting activity-driven neuronal and synaptic plasticity changed discussions about what might underlie learning from speculation into something much more concrete. Equally, however, the relationship between the discovery of LTP and research on the neurobiology of learning and memory has been reciprocal; for it is also true that studies of the psychological, anatomical and neurochemical basis of memory provided a developing and critical intellectual context for the physiological discovery. The emerging concept of multiple memory systems, from 1970 onwards, paved the way for the development of new behavioural and cognitive tasks, including the watermaze described in this paper. The use of this task in turn provided key evidence that pharmacological interference with an LTP induction mechanism would also interfere with learning, a finding that was by no means a foregone conclusion. This reciprocal relationship between studies of LTP and the neurobiology of memory helped the physiological phenomenon to be recognized as a major discovery.     

Spatially addressed combinatorial protein libraries for recombinant antibody discovery and optimization

Antibody discovery typically uses hybridoma- or display-based selection approaches, which lack the advantages of directly screening spatially addressed compound libraries as in small-molecule discovery. Here we apply the latter strategy to antibody discovery, using a library of ～10,000 human germline antibody Fabs created by de novo DNA synthesis and automated protein expression and purification. In multiplexed screening assays, we obtained specific hits against seven of nine antigens. Using sequence-activity relationships and iterative mutagenesis, we optimized the binding affinities of two hits to the low nanomolar range. The matured Fabs showed full and partial antagonism activities in cell-based assays. Thus, protein drug leads can be discovered using surprisingly small libraries of proteins with known sequences, questioning the requirement for billions of members in an antibody discovery library. This methodology also provides sequence, expression and specificity information at the first step of the discovery process, and could enable novel antibody discovery in functional screens.