The Role of Bioinformatics in Drug Discovery: A Comprehensive Overview

Bioinformatics plays a crucial role in various aspects of drug discovery, drug metabolism, and pharmacology. In drug discovery, bioinformatics enables the efficient analysis and interpretation of large-scale biological data, facilitating target identification, lead compound optimization, and prediction of drug-target interactions. It aids in the identification and characterization of potential drug targets through genomic and proteomic analyses. Additionally, bioinformatics assists in the prediction of drug metabolism and pharmacokinetic properties, offering insights into the safety and efficacy of potential drug candidates. Furthermore, it contributes to pharmacology by enabling the analysis of drug-drug interactions, adverse drug reactions, and personalized medicine approaches. The integration of computational tools and algorithms with biological and chemical data has accelerated the drug discovery process, improved success rates, and reduced costs. Bioinformatics has become an indispensable tool in the development of novel therapeutics and the optimization of drug efficacy and safety. This book chapter elucidates the profound impact of bioinformatics in drug metabolism and pharmacology, emphasizing the transformative potential it holds for the future of pharmaceutical research, ultimately improving patient outcomes and bringing innovative therapies

Five flavonoids from Lucerne varieties

Alfalfa (Fabaceae) is known as perennial herbaceous leguminous plant species that originated in southwestern Asia and is used as a folk medicine for the treatment of various ailments. The upper ground part of Lucerne contains phenolic compound such as flavonoids and others, which exhibit biological activities. This study aimed to determine five widely known flavonoids in extracts 20 alfalfa varieties herb at the Ukrainian steppe growing. 50 seeds of the same size were selected from twenty varieties of alfalfa from different countries, and cultivated in controlled areas of the southern part of the Left Bank of Ukraine at the border of forest-steppe and steppe zones (Zaporizhzhya, Ukraine) from April to June, with 15 °C/ 07 °C (day/night), 14 h/10 h (light/dark) and 60–65% relative humidity. The content of flavonoids was found unequable in ethanol extracts. The chemical compositions and their content were assessed by ultrahigh-performance liquid chromatography. The content of five flavonoids was different in the 20 alfalfa varieties raw materials. Umbelliferone was found high in ethanol extract of Mongolian colorful hybrid (Mongolia, 0.23 mg/g). Four sorts have not contained umbelliferone: Кisvardai (Hungary), Nizona (Cuba), Тanhuato (Mexico), and Mesopotamian (Iraq). The leader from cinaroside content was sort Commercial 2-52-75 of UK origin. Routine has been found in the highest quantities in WL 50 from the USA. Ferganska 700 from Uzbekistan was the leader in luteolin content and Кisvardai, Hungary was the leader in an average of kaempferol content (0.030 mg/g). The present article comprises the hierarchical cluster analyses from the data flavonoid assay. In fact, a real method has been obtained for the targeted production of valuable biologically active components with a high content from plant sources

On the eve of artificial minds

I review recent technological, empirical, and theoretical developments related to building sophisticated cognitive machines. I suggest that rapid growth in robotics, brain-like computing, new theories of large-scale functional modeling, and financial resources directed at this goal means that there will soon be a significant increase in the abilities of artificial minds. I propose a specific timeline for this development over the next fifty years and argue for its plausibility. I highlight some barriers to the development of this kind of technology, and discuss the ethical and philosophical consequences of such a development. I conclude that researchers in this field, governments, and corporations must take care to be aware of, and willing to discuss, both the costs and benefits of pursuing the construction of artificial minds

Novel Harmonic Regularization Approach for Variable Selection in Cox’s Proportional Hazards Model

Variable selection is an important issue in regression and a number of variable selection methods have been proposed involving nonconvex penalty functions. In this paper, we investigate a novel harmonic regularization method, which can approximate nonconvex Lq  (1/2<q<1) regularizations, to select key risk factors in the Cox’s proportional hazards model using microarray gene expression data. The harmonic regularization method can be efficiently solved using our proposed direct path seeking approach, which can produce solutions that closely approximate those for the convex loss function and the nonconvex regularization. Simulation results based on the artificial datasets and four real microarray gene expression datasets, such as real diffuse large B-cell lymphoma (DCBCL), the lung cancer, and the AML datasets, show that the harmonic regularization method can be more accurate for variable selection than existing Lasso series methods

Decoding heterogeneous big data in an integrative way

Biotechnologies in post-genomic era, especially those that generate data in high-throughput, bring opportunities and challenges that are never faced before. And one of them is how to decode big heterogeneous data for clues that are useful for biological questions. With the exponential growth of a variety of data, comes with more and more applications of systematic approaches that investigate biological questions in an integrative way. Systematic approaches inherently require integration of heterogeneous information, which is urgently calling for a lot more efforts. In this thesis, the effort is mainly devoted to the development of methods and tools that help to integrate big heterogeneous information. In Chapter 2, we employed a heuristic strategy to summarize/integrate genes that are essential for the determination of mouse retinal cells in the format of network. These networks with experimental evidence could be rediscovered in the analysis of high-throughput data set and thus would be useful in the leverage of high-throughput data. In Chapter 3, we described EnRICH, a tool that we developed to help qualitatively integrate heterogeneous intro-organism information. We also introduced how EnRICH could be applied to the construction of a composite network from different sources, and demonstrated how we used EnRICH to successfully prioritize retinal disease genes. Following the work of Chapter 3 (intro-organism information integration), in Chapter 4 we stepped to the development of method and tool that can help deal with inter-organism information integration. The method we proposed is able to match genes in a one-to-one fashion between any two genomes. In summary, this thesis contributes to integrative analysis of big heterogeneous data by its work on the integration of intro- and inter-organism information

The role of Heparin-binding proteins in normal pancreas and acute pancreatitis

Acute pancreatitis (AP) is a leading cause for hospitalisation and has significant quality of life implications for the patient and cost implications for the National Health Service. Although most episodes of AP are mild and self-limiting, the severe form of the disease is associated with a high mortality. In the absence of definitive treatment, management is mainly supportive. There is an urgent need to develop more effective biomarkers and drugs to manage AP. Genome-wide studies have demonstrated that proteins that bind to heparin (HBPs) form highly interconnected networks which are functionally important in health and disease. It was hypothesized that this is true in the pancreas and in AP. Testing this hypothesis, using mRNA as a proxy for protein, it was shown that HBPs constitute an important extracellular sub-proteome within the normal pancreas and in major pancreatic diseases that is likely to provide a rich repository of potential biomarkers and drug targets. Building upon this work, a proteomic analysis of HBPs in normal pancreas (NP) and in caerulein-induced mouse AP was undertaken. This has more than doubled the number of HBPs to 883, with 460 new HBPs identified. These may represent the most interconnected set of extracellular proteins and therefore with the greatest regulatory potential. Non canonical HBPs such as NDUFS4, NDUFS6, NDUFS7, NDUFS8, NDUFA9, NDUFA10, NDUFA9 and NDUFA10 were identified and found to be underexpressed in AP as compared to NP. These may have potential moonlighting roles, not previously known. By virtue of being extracellular and binding to heparin, HBPs are accessible and are potential biomarkers and drug targets in AP. In addition to identifying existing biomarkers in AP such as pancreatic amylase, a number of HBPs with biomarkers potential such as HRG, CD14 and FN1 were identified and need further investigation. HBPs such as SERPINC1, VEGFA and PIP5K1C need further evaluation in drug development. These along with modified heparins, heparin mimetics and matrix therapy in AP provide exciting areas for future research

TARGET-DIRECTED BIOSYNTHETIC EVOLUTION: REDIRECTING PLANT EVOLUTION TO GENOMICALLY OPTIMIZE A PLANT’S PHARMACOLOGICAL PROFILE

The dissertation describes a novel method for plant drug discovery based on mutation and selection of plant cells. Despite the industry focus on chemical synthesis, plants remain a source of potent and complex bioactive metabolites. Many of these have evolved as defensive compounds targeted on key proteins in the CNS of herbivorous insects, for example the insect dopamine transporter (DAT). Because of homology with the human DAT protein some of these metabolites have high abuse potential, but others may be valuable in treating drug dependence. This dissertation redirects the evolution of a native Lobelia species toward metabolites with greater activity at this therapeutic target, i.e. the human DAT. This was achieved by expressing the human DAT protein in transgenic plant cells and selecting gain-of-function mutants for survival on medium containing a neurotoxin that is accumulated by the human DAT. This created a sub-population of mutants with increased DAT inhibitory activity. Some of the active metabolites in these mutants are novel (i.e. not detectable in wild-type cells). Others are cytoprotective, and also protect DAergic neurons against the neurotoxin. This provides proof-of-concept for a novel plant drug discovery platform, which is applicable to many different therapeutic target proteins and plant species

Synergistic behaviour of Salvia and Notoginseng species in vascular diseases

As the mainstay and principal form of traditional Chinese medicine (TCM), Chinese herbal medicine (CHM) has been the subject of growing interest and popular use worldwide. However, its unique philosophy, diagnosis and prescription are completely different from Western Medicine (WM), which has brought great challenges for the quality standardisation, safety control, and efficacy evaluation confronting its development in a modernised scientific manner. This PhD project aims to address challenges that revolve around the efficacy research of CHM. Based on CHM theory, the key mechanism of the efficacy of CHM is the synergistic interactions among multiple herbal ingredients in a formula to reach an optimised therapeutic effect, multi-target mode of actions and reduced potential side effects. Several rigorous analytical methods such as combination index (CI), isobolographic analysis and systems biology are designed for the quantitative evaluations of synergistic effects in pharmaceutical combination therapy, and have also been utilised for the study of CHM. Among them, CI and isobologram models are applied for studying the interactions of a small number of active components or herbal extracts on the same target or receptor in which their chemical and pharmacological properties are well defined. A systems biology model may also be used to analyse multi-component, multi-target actions in combinational therapy. However, following a systematic review, it is apparent that the current literature on synergistic study of CHM is still at an early stage. Based on previous studies, we hypothesised that a platform to systematically analyse synergistic interactions of herbal compounds can be established by modern bioassays and scientific models (e.g., CI and isobologram approaches). Herb-pairs are the basic unit for the Chinese herbal formulae. Salvia Miltiorrhiza Radix et Rhizoma and Notoginseng Radix et Rhizome (known as Danshen [DS] and Sanqi [SQ] in TCM) has been one of the most frequently prescribed herb pairs in TCM clinics for cardiovascular disease management for over 30 years in Asian countries. However, very limited mechanistic studies on the combinational benefits on the complex pathological mechanisms of vascular diseases are available, despite the fact that the bioactivities for a single extract have been well studied. Therefore, the DS-SQ herb-pair was selected as a case study to address the issue of synergistic activities in CHM