<i>In silico</i> approaches and tools for the prediction of drug metabolism and fate: A review

Kazmi, Sayada Reemsha,Jun, Ren,Yu, Myeong-Sang,Jung, Chanjin,Na, Dokyun Elsevier 2019 Computers in biology and medicine Vol.106 No.-

<P><B>Abstract</B></P> <P>The fate of administered drugs is largely influenced by their metabolism. For example, endogenous enzyme–catalyzed conversion of drugs may result in therapeutic inactivation or activation or may transform the drugs into toxic chemical compounds. This highlights the importance of drug metabolism in drug discovery and development, and accounts for the wide variety of experimental technologies that provide insights into the fate of drugs. In view of the high cost of traditional drug development, a number of computational approaches have been developed for predicting the metabolic fate of drug candidates, allowing for screening of large numbers of chemical compounds and then identifying a small number of promising candidates. In this review, we introduce <I>in silico</I> approaches and tools that have been developed to predict drug metabolism and fate, and assess their potential to facilitate the virtual discovery of promising drug candidates. We also provide a brief description of various recent models for predicting different aspects of enzyme-drug reactions and provide a list of recent <I>in silico</I> tools used for drug metabolism prediction.</P> <P><B>Highlights</B></P> <P> <UL> <LI> <I>In silico</I> approaches and tools for predicting drug metabolism and fate are reviewed. </LI> <LI> QSAR, machine learning, and computational docking/molecular dynamics are described. </LI> <LI> Computational models for predicting metabolic enzymatic reactions are summarized. </LI> <LI> A list of tools for <I>in silico</I> prediction is provided. </LI> <LI> Concerns and limitations of predictive model construction are outlined. </LI> </UL> </P>

Natural products used as a chemical library for protein–protein interaction targeted drug discovery

Jin, Xuemei,Lee, Kyungro,Kim, Nam Hee,Kim, Hyun Sil,Yook, Jong In,Choi, Jiwon,No, Kyoung Tai Elsevier 2018 Journal of molecular graphics & modelling Vol.79 No.-

<P><B>Abstract</B></P> <P>Protein–protein interactions (PPIs), which are essential for cellular processes, have been recognized as attractive therapeutic targets. Therefore, the construction of a PPI-focused chemical library is an inevitable necessity for future drug discovery. Natural products have been used as traditional medicines to treat human diseases for millennia; in addition, their molecular scaffolds have been used in diverse approved drugs and drug candidates. The recent discovery of the ability of natural products to inhibit PPIs led us to use natural products as a chemical library for PPI-targeted drug discovery. In this study, we collected natural products (NPDB) from non-commercial and in-house databases to analyze their similarities to small-molecule PPI inhibitors (iPPIs) and FDA-approved drugs by using eight molecular descriptors. Then, we evaluated the distribution of NPDB and iPPIs in the chemical space, represented by the molecular fingerprint and molecular scaffolds, to identify the promising scaffolds, which could interfere with PPIs. To investigate the ability of natural products to inhibit PPI targets, molecular docking was used. Then, we predicted a set of high-potency natural products by using the iPPI-likeness score based on a docking score-weighted model. These selected natural products showed high binding affinities to the PPI target, namely XIAP, which were validated in an <I>in vitro</I> experiment. In addition, the natural products with novel scaffolds might provide a promising starting point for further medicinal chemistry developments. Overall, our study shows the potency of natural products in targeting PPIs, which might help in the design of a PPI-focused chemical library for future drug discovery.</P> <P><B>Highlights</B></P> <P> <UL> <LI> This paper provides <I>in silico</I> drug discovery strategy to identify natural products capable to inhibit the protein–protein interactions. </LI> <LI> The predicted PPI inhibitor-like natural products were validated in an in vitro experiment. </LI> <LI> The XIAP inhibitor LENP0044 could be used as a potent template for further chemical optimization. </LI> <LI> Natural products can be used as potent candidates in the design of a PPI-focused chemical library for drug discovery. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

김현호,김은영,이인구,배봉성,박민수,남호정 한국생물공학회 2020 Biotechnology and Bioprocess Engineering Vol.25 No.6

As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.

초기 신약개발 단계에서의 신약후보물질 최적화를 위한 물성 스크리닝 방법 검증 및 프로파일링

이진석(Jinseok Lee),안성훈(Sung-Hoon Ahn) 대한약학회 2018 약학회지 Vol.62 No.4

Drug-like properties based on physicochemical properties are important to confer good ADME/PK char-acteristics for sufficiently effective efficacy and safety on preclinical and clinical trials. Therefore, accurate estimation and optimization of physicochemical properties such as ionization constant, lipophilicity, permeability, and solubility are import-ant factors for pharmacokinetic properties including clearance, half-life, bioavailability, drug-drug interactions. This study was performed to validate screening method of physicochemical properties. The commercially available drugs were used to validate analytical method system of physicochemical properties and experimental values were compared with literature values and in silico predictions. The experimental pKa values were in very good accordance with literature values in both case of pKa PRO (r² = 0.82) and GLpKa (r² = 0.87). Experimental values and in silico predictions of lipophilicity were also in very good accordance with literature values (r² > 0.82). Experimental physicochemical values of KR-62980 as a new drug candidate showed similar values to in silico predictions. Finally, screenings of physicochemical properties can be applied well and this result may cause lowering cost and accelerating screening speed for the integration of ADME/PK screening data in early stage of new drug discovery.

Prediction of Drug–Drug Interactions by Using Profile Fingerprint Vectors and Protein Similarities

Selma Dere,Serkan Ayvaz 대한의료정보학회 2020 Healthcare Informatics Research Vol.26 No.1

Objectives: Drug–drug interaction (DDI) is a vital problem that threatens people’s health. However, the prediction of DDIs through in-vivo experiments is not only extremely costly but also difficult as many serious side effects are hard to detect in in-vivo and in-vitro settings. The aim of this study was to assess the effectiveness of similarity-based in-silico computational DDI prediction approaches and to provide a cost effective and scalable solution to predict potential DDIs. Methods: In this study, widely known similarity-based computational DDI prediction methods were utilized to discover novel potential DDIs. More specifically, known interactions, drug targets, adverse effects, and protein similarities of drug pairs were used to construct drug fingerprints for the prediction of DDIs. Results: Using the drug interaction profile, our approach achieved an area under the curve (AUC) of 0.975 in the prediction of a potential DDI. The drug adverse effect profile and protein profile similarity-based methods resulted in AUC values of 0.685 and 0.895, respectively, in the prediction of DDIs. Conclusions: In this study, we developed a computational approach to the prediction of potential drug interactions. The performance of the similarity-based computational methods was comparatively evaluated using a comprehensive real-world DDI dataset. The evaluations showed that the drug interaction profile information is a better predictor of DDIs compared to drug adverse effects and protein similarities among DDI pairs.

A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition

Gachloo, Mina,Wang, Yuxing,Xia, Jingbo Korea Genome Organization 2019 Genomics & informatics Vol.17 No.2

Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different sources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or matrix decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.

A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition

Mina Gachloo,Yuxing Wang,Jingbo Xia 한국유전체학회 2019 Genomics & informatics Vol.17 No.2

Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different sources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or matrix decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.

Structural basis for the inhibition of <i>Mycobacterium tuberculosis</i> <small>L</small> , <small>D</small> -transpeptidase by meropenem, a drug effective against extensively drug-resistant strains

Kim, Hyoun Sook,Kim, Jieun,Im, Ha Na,Yoon, Ji Young,An, Doo Ri,Yoon, Hye Jin,Kim, Jin Young,Min, Hye Kyeoung,Kim, Soon-Jong,Lee, Jae Young,Han, Byung Woo,Suh, Se Won International Union of Crystallography 2013 Acta crystallographica. Section D, Biological crys Vol.69 No.3

<▼1><P>The crystal structure of <I>M. tuberculosis</I><SMALL>L</SMALL>,<SMALL>D</SMALL>-transpeptidase (Ldt<SUB>Mt2</SUB>; Rv2518c) has been determined in both ligand-free and meropenem-bound forms. The detailed view of the interactions between meropenem and Ldt<SUB>Mt2</SUB> will be useful in structure-guided discovery of new antituberculosis drugs.</P></▼1><▼2><P>Difficulty in the treatment of tuberculosis and growing drug resistance in <I>Mycobacterium tuberculosis</I> (<I>Mtb</I>) are a global health issue. Carbapenems inactivate <SMALL>L</SMALL>,<SMALL>D</SMALL>-transpeptidases; meropenem, when administered with clavulanate, showed <I>in vivo</I> activity against extensively drug-resistant <I>Mtb</I> strains. Ldt<SUB>Mt2</SUB> (Rv2518c), one of two functional <SMALL>L</SMALL>,<SMALL>D</SMALL>-transpeptidases in <I>Mtb</I>, is predominantly expressed over Ldt<SUB>Mt1</SUB> (Rv0116c). Here, the crystal structure of N-terminally truncated Ldt<SUB>Mt2</SUB> (residues Leu131–Ala408) is reported in both ligand-free and meropenem-bound forms. The structure of meropenem-inhibited Ldt<SUB>Mt2</SUB> provides a detailed structural view of the interactions between a carbapenem drug and <I>Mtb</I><SMALL>L</SMALL>,<SMALL>D</SMALL>-transpeptidase. The structures revealed that the catalytic <SMALL>L</SMALL>,<SMALL>D</SMALL>-­transpeptidase domain of Ldt<SUB>Mt2</SUB> is preceded by a bacterial immunogloblin-like Big_5 domain and is followed by an extended C-terminal tail that interacts with both domains. Furthermore, it is shown using mass analyses that meropenem acts as a suicide inhibitor of Ldt<SUB>Mt2</SUB>. Upon acylation of the catalytic Cys354 by meropenem, the ‘active-site lid’ undergoes a large conformational change to partially cover the active site so that the bound meropenem is accessible to the bulk solvent <I>via</I> three narrow paths. This work will facilitate structure-guided discovery of <SMALL>L</SMALL>,<SMALL>D</SMALL>-transpeptidase inhibitors as novel antituberculosis drugs against drug-resistant <I>Mtb</I>.</P></▼2>

Next-generation antimicrobials: from chemical biology to first-in-class drugs

Michelle Lay Teng Ang,Paul Murima,Kevin Pethe 대한약학회 2015 Archives of Pharmacal Research Vol.38 No.9

The global emergence of multi-drug resistantbacteria invokes an urgent and imperative necessity for theidentification of novel antimicrobials. The general lack ofsuccess in progressing novel chemical entities from targetbaseddrug screens have prompted calls for radical andinnovative approaches for drug discovery. Recent developmentsin chemical biology and target deconvolutionstrategies have revived interests in the utilization of wholecellphenotypic screens and resulted in several successstories for the discovery and development novel drugcandidates and target pathways. In this review, we presentand discuss recent chemical biology approaches focusingon the discovery of novel targets and new lead moleculesfor the treatment of human bacterial and protozoaninfections.

Induced Pluripotent Stem Cell Research: A Revolutionary Approach to Face the Challenges in Drug Screening

Minjung Song,조쌍구,Saswati Paul,임혜진,아브달아메드 대한약학회 2012 Archives of Pharmacal Research Vol.35 No.2

Discovery of induced pluripotent stem (iPS) cells in 2006 provided a new path for cell transplantation and drug screening. The iPS cells are stem cells derived from somatic cells that have been genetically reprogrammed into a pluripotent state. Similar to embryonic stem (ES) cells, iPS cells are capable of differentiating into three germ layers, eliminating some of the hurdles in ES cell technology. Further progress and advances in iPS cell technology, from viral to non-viral systems and from integrating to non-integrating approaches of foreign genes into the host genome, have enhanced the existing technology, making it more feasible for clinical applications. In particular, advances in iPS cell technology should enable autologous transplantation and more efficient drug discovery. Cell transplantation may lead to improved treatments for various diseases, including neurological, endocrine, and hepatic diseases. In studies on drug discovery, iPS cells generated from patient-derived somatic cells could be differentiated into specific cells expressing specific phenotypes, which could then be used as disease models. Thus, iPS cells can be helpful in understanding the mechanisms of disease progression and in cell-based efficient drug screening. Here, we summarize the history and progress of iPS cell technology, provide support for the growing interest in iPS cell applications with emphasis on practical uses in cell-based drug screening, and discuss some challenges faced in the use of this technology.