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computational prediction of formulation strategies for beyond-rule-of-5 compounds

ADVANCED DRUG DELIVERY REVIEWS 2016年 101卷 6-21页

作者： Bergstrom, Christel A. S. Charman, William N. Porter, Christopher J. H. Monash Univ Drug Delivery Disposit & Dynam Monash Inst Pharmaceut Sci 381 Royal Parade Parkville Vic 3052 Australia Uppsala Univ Dept Pharm Uppsala Biomed Ctr POB 580 SE-75123 Uppsala Sweden Monash Univ ARC Ctr Excellence Convergent Nanobio Sci & Techn Monash Inst Pharmaceut Sci 381 Royal Parade Parkville Vic 3052 Australia

The physicochemical properties of some contemporary drug candidates are moving towards higher molecular weight, and coincidentally also higher lipophilicity in the quest for biological selectivity and specificity. These physicochemical properties move the compounds towards beyond rule-of-5 (B-r-o-5) chemical space and often result in lower water solubility. For such B-r-o-5 compounds non-traditional delivery strategies (i.e. those other than conventional tablet and capsule formulations) typically are required to achieve adequate exposure after oral administration. In this review, we present the current status of computational tools for prediction of intestinal drug absorption, models for prediction of the most suitable formulation strategies for B-r-o-5 compounds and models to obtain an enhanced understanding of the interplay between drug, formulation and physiological environment. In silico models are able to identify the likely molecular basis for low solubility in physiologically relevant fluids such as gastric and intestinal fluids. With this baseline information, a formulation scientist can, at an early stage, evaluate different orally administered, enabling formulation strategies. Recent computational models have emerged that predict glass-forming ability and crystallisation tendency and therefore the potential utility of amorphous solid dispersion formulations. Further, computational models of loading capacity in lipids, and therefore the potential for formulation as a lipid-based formulation, are now available. Whilst such tools are useful for rapid identification of suitable formulation strategies, they do not reveal drug localisation and molecular interaction patterns between drug and excipients. For the latter, Molecular Dynamics simulations provide an insight into the interplay between drug, formulation and intestinal fluid. These different computational approaches are reviewed. Additionally, we analyse the molecular requirements of different targets, si

关键词： Lipophilic targets Solubility Permeability ADMET Enabling formulations Beyond rule-of-five Biopharmaceutical performance computational prediction

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computational prediction of Superlubric Layered Heterojunctions

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ACS APPLIED MATERIALS & INTERFACES 2021年第28期13卷 33600-33608页

作者： Gao, Enlai Wu, Bozhao Wang, Yelingyi Jia, Xiangzheng Ouyang, Wengen Liu, Ze Wuhan Univ Sch Civil Engn Dept Engn Mech Wuhan 430072 Hubei Peoples R China

Structural superlubricity has attracted increasing interest in modern tribology. However, experimental identification of superlubric interfaces among the vast number of heterojunctions is a trial-and-error and time-consuming approach. In this work, based on the requirements on the in-plane stiffnesses of layered materials and the interfacial interactions at the sliding incommensurate interfaces of heterojunctions for structural superlubricity, we propose criteria for predicting structural superlubricity between heterojunctions. Based on these criteria, we identify 61 heterojunctions with potential superlubricity features from 208 candidates by screening the data of first-principles calculations. This work provides a universal route for accelerating the discovery of new superlubric heterojunctions.

关键词： computational prediction 2D heterojunction structural superlubricity friction first-principles calculations

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computational prediction of NO-dependent posttranslational modifications in plants: Current status and perspectives

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PLANT PHYSIOLOGY AND BIOCHEMISTRY 2021年 167卷 851-861页

作者： Kolbert, Zsuzsanna Lindermayr, Christian Univ Szeged Dept Plant Biol Kozep Fasor 52 H-6726 Szeged Hungary German Res Ctr Environm Hlth Inst Biochem Plant Pathol Helmholtz Zentrum Munchen Ingolstaedter Landstr 1 D-85764 Munich Germany

The perception and transduction of nitric oxide (NO) signal is achieved by NO-dependent posttranslational modifications (PTMs) among which S-nitrosation and tyrosine nitration has biological significance. In plants, 100-1000 S-nitrosated and tyrosine nitrated proteins have been identified so far by mass spectrometry. The determination of NO-modified protein targets/amino acid residues is often methodologically challenging. In the past decade, the growing demand for the knowledge of S-nitrosated or tyrosine nitrated sites has motivated the introduction of bioinformatics tools. For predicting S-nitrosation seven computational tools have been developed (GPS-SNO, SNOSite, iSNO-PseACC, iSNO-AAPAir, PSNO, PreSNO, RecSNO). Four predictors have been developed for indicating tyrosine nitration sites (GPS-YNO2, iNitro-Tyr, PredNTS, iNitroY-Deep), and one tool (DeepNitro) predicts both NO-dependent PTMs. The advantage of these computational tools is the fast provision of large amount of information. In this review, the available software tools have been tested on plant proteins in which S-nitrosated or tyrosine nitrated sites have been experimentally identified. The predictors showed distinct performance and there were differences from the experimental results partly due to the fact that the threedimensional protein structure is not taken into account by the computational tools. Nevertheless, the predictors excellently establish experiments, and it is suggested to apply all available tools on target proteins and compare their results. In the future, computational prediction must be developed further to improve the precision with which S-nitrosation/tyrosine nitration-sites are identified.

关键词： computational prediction Nitric oxide Posttranslational modification S-Nitrosation Tyrosine nitration

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computational prediction of micrornas and their target genes in rainbow trout (Oncorhynchus mykiss)

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RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2016年第3期42卷 282-293页

作者： Huang, Yong Ma, Xiu Ying Yang, You Bing Gao, Xiao Chan Ren, Hong Tao Zhang, Chun Nuan Henan Univ Sci & Technol Coll Anim Sci & Technol Luoyang Peoples R China

MicroRNAs (miRNA) are non-coding small RNA molecules consisting of 20-24 nucleotides (nt), which regulate gene expression negatively at the post-transcriptional levels depending on the extent of complementation between miRNA and mRNA in a variety of eukaryotic organisms. They have attracted increasing attention recently. To date, a total of 1637 mature miRNAs from fishes have been deposited in the miRBase database (Release 21) and they are just a small portion of the miRNAs described in fish species. Especially in rainbow trout (Oncorhynchus mykiss) miRNAs, it is far from the numbers of miRNAs found in other fishes and many more miRNA remain to be discovered. In present study, we used known animal miRNAs to systematically search miRNA homologs in available expressed sequence tags (EST), genomic survey sequences (GSS), and transcript sequence assemblies (TSA) of O. mykiss based on the conservation of miRNA sequences. Following the filtering with a combination of stringent criteria, 39 miRNAs belonging to 25 independent families were identified, of which 14 are novel identified miRNAs firstly in O. mykiss. The randomly selected 13 miRNAs were verified by stem-loop RT-PCR indicating that the prediction method that we used to identify the miRNAs was effective. Furthermore, total of 94 target genes were predicted and their probable functions were illustrated. Their target genes are involved in transcription factors, development, metabolism, signaling, immunity, and cell division. The current study has provided an update on O. mykiss miRNAs and their targets, which will be helpful for future research on miRNA-mediated gene regulation in this important fish species.

关键词： microRNA Oncorhynchus mykiss computational prediction target gene

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computational prediction of drug solubility in water-based systems: Qualitative and quantitative approaches used in the current drug discovery and development setting

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INTERNATIONAL JOURNAL OF PHARMACEUTICS 2018年第1-2期540卷 185-193页

作者： Bergstrom, Christel A. S. Larsson, Per Uppsala Univ Dept Pharm Biomed Ctr POB 580 SE-75123 Uppsala Sweden

In this review we will discuss recent advances in computational prediction of solubility in water-based solvents. Our focus is set on recent advances in predictions of biorelevant solubility in media mimicking the human intestinal fluids and on new methods to predict the thermodynamic cycle rather than prediction of solubility in pure water through quantitative structure property relationships (QSPR). While the literature is rich in QSPR models for both solubility and melting point, a physicochemical property strongly linked to the solubility, recent advances in the modelling of these properties make use of theory and computational simulations to better predict these properties or processes involved therein (e.g. solid state crystal lattice packing, dissociation of molecules from the lattice and solvation). This review serves to provide an update on these new approaches and how they can be used to more accurately predict solubility, and also importantly, inform us on molecular interactions and processes occurring during drug dissolution and solubilisation.

关键词： computational prediction Solubility Solid state Intestinal fluid Quantitative structure property relationships Molecular dynamics simulations

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computational prediction of Drug Solubility in Lipid Based Formulation Excipients

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PHARMACEUTICAL RESEARCH 2013年第12期30卷 3225-3237页

作者： Persson, Linda C. Porter, Christopher J. H. Charman, William N. Bergstrom, Christel A. S. Uppsala Univ Dept Pharm Uppsala Biomed Ctr Drug Optimizat & Pharmaceut Profiling Platform S-75123 Uppsala Sweden Monash Univ Monash Inst Pharmaceut Sci Parkville Vic 3052 Australia

To investigate if drug solubility in pharmaceutical excipients used in lipid based formulations (LBFs) can be predicted from physicochemical properties. Solubility was measured for 30 structurally diverse drug molecules in soybean oil (SBO, long-chain triglyceride;TG(LC)), Captex355 (medium-chain triglyceride;TG(MC)), polysorbate 80 (PS80;surfactant) and PEG400 co-solvent and used as responses during PLS model development. Melting point and calculated molecular descriptors were used as variables and the PLS models were validated with test sets and permutation tests. Solvation capacity of SBO and Captex355 was equal on a mol per mol scale (R (2) = 0.98). A strong correlation was also found between PS80 and PEG400 (R (2) = 0.85), identifying the significant contribution of the ethoxylation for the solvation capacity of PS80. In silico models based on calculated descriptors were successfully developed for drug solubility in SBO (R (2) = 0.81, Q (2) = 0.76) and Captex355 (R (2) = 0.84, Q (2) = 0.80). However, solubility in PS80 and PEG400 were not possible to quantitatively predict from molecular structure. Solubility measured in one excipient can be used to predict solubility in another, herein exemplified with TG(MC) versus TG(LC), and PS80 versus PEG400. We also show, for the first time, that solubility in TG(MC) and TG(LC) can be predicted from rapidly calculated molecular descriptors.

关键词： computational prediction lipid based formulation loading capacity molecular properties solubility

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computational prediction of kink properties of helices in membrane proteins

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JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN 2014年第2期28卷 99-109页

作者： Mai, T. -L. Chen, C. -M. Natl Taiwan Normal Univ Dept Phys Taipei 116 Taiwan

We have combined molecular dynamics simulations and fold identification procedures to investigate the structure of 696 kinked and 120 unkinked transmembrane (TM) helices in the PDBTM database. Our main aim of this study is to understand the formation of helical kinks by simulating their quasi-equilibrium heating processes, which might be relevant to the prediction of their structural features. The simulated structural features of these TM helices, including the position and the angle of helical kinks, were analyzed and compared with statistical data from PDBTM. From quasi-equilibrium heating processes of TM helices with four very different relaxation time constants, we found that these processes gave comparable predictions of the structural features of TM helices. Overall, 95 % of our best kink position predictions have an error of no more than two residues and 75 % of our best angle predictions have an error of less than 15A degrees. Various structure assessments have been carried out to assess our predicted models of TM helices in PDBTM. Our results show that, in 696 predicted kinked helices, 70 % have a RMSD less than 2 , 71 % have a TM-score greater than 0.5, 69 % have a MaxSub score greater than 0.8, 60 % have a GDT-TS score greater than 85, and 58 % have a GDT-HA score greater than 70. For unkinked helices, our predicted models are also highly consistent with their crystal structure. These results provide strong supports for our assumption that kink formation of TM helices in quasi-equilibrium heating processes is relevant to predicting the structure of TM helices.

关键词： Helical kinks Molecular dynamics simulation Fold identification computational prediction Quasi-equilibrium heating

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computational prediction of nucleosome positioning by calculating the relative fragment frequency index of nucleosomal sequences

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FEBS LETTERS 2010年第8期584卷 1498-1502页

作者： Ogawa, Ryu Kitagawa, Noriyuki Ashida, Hiroki Saito, Rintaro Tomita, Masaru Keio Univ Fac Environm & Informat Studies Fujisawa Kanagawa 2528520 Japan Keio Univ Inst Adv Biosci Tsuruoka Yamagata 9970017 Japan Keio Univ Grad Sch Media & Governance Syst Biol Program Fujisawa Kanagawa 2528520 Japan

We developed an accurate method to predict nucleosome positioning from genome sequences by refining the previously developed method of Peckham et al. ( 2007) [ 19]. Here, we used the relative fragment frequency index we developed and a support vector machine to screen for nucleosomal and linker DNA sequences. Our twofold cross-validation revealed that the accuracy of our method based on the area under the receiver operating characteristic curve was 81%, whereas that of Peckham's method was 75% when both of two nucleosomal sequence data obtained from independent experiments were used for validation. We suggest that our method is more effective in predicting nucleosome positioning. (C) 2010 Federation of European Biochemical Societies. Published by Elsevier B. V. All rights reserved.

关键词： Nucleosome computational prediction Support vector machine

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computational prediction of crucial genes involved in gonorrhea infection and neoplastic cell transformation: A multiomics approach

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MICROBIAL PATHOGENESIS 2024年 193卷 106770页

作者： Ravindranath, B. S. Ananya, G. Kumar, C. Hema Ramirez, D. C. Mejiba, S. E. Gomez Manipal Acad Higher Educ MAHE Manipal Inst Technol Manipal 576104 Karnataka India Manipal Acad Higher Educ MAHE Manipal Sch Life Sci Manipal 576104 Karnataka India Dayananda Sagar Coll Engn Dept Biotechnol Shavige Malleshwara Hills Bangalore 560111 Karnataka India CCT San Luis Natl Univ San Luis Lab Expt & Translat Med RA-5700 San Luis San Luis Argentina CCT San Luis Natl Univ San Luis Lab Nutr & Expt Therapeut RA-5700 San Luis San Luis Argentina

Neisseria gonorrheae, the causative agent of genitourinary infections, has been associated with asymptomatic or recurrent infections and has the potential to form biofilms and induce inflammation and cell transformation. Herein, we aimed to use computational analysis to predict novel associations between chronic inflammation caused by gonorrhea infection and neoplastic transformation. Prioritization and gene enrichment strategies based on virulence and resistance genes utilizing essential genes from the DEG and PANTHER databases, respectively, were performed. Using the STRING database, protein-protein interaction networks were constructed with 55 nodes of bacterial proteins and 72 nodes of proteins involved in the host immune response. MCODE and cytoHubba were used to identify 12 bacterial hub proteins (murA, murB, murC, murD, murE, purN, purL, thyA, uvrB, kdsB, lpxC, and ftsH) and 19 human hub proteins, of which TNF, STAT3 and AKT1 had high significance. The PPI networks are based on the connectivity degree (K), betweenness centrality (BC), and closeness centrality (CC) values. Hub genes are vital for cell survival and growth, and their significance as potential drug targets is discussed. This computational study provides a comprehensive understanding of inflammation and carcinogenesis pathways that are activated during gonorrhea infection.

关键词： Neisseria gonorrheae computational prediction Drug-resistance gene Protein -protein interaction Inflammation Carcinogenesis

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computational prediction of manually gated rare cells in flow cytometry data

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CYTOMETRY PART A 2015年第7期87A卷 594-602页

作者： Qiu, Peng Georgia Inst Technol Dept Biomed Engn Atlanta GA 30332 USA Emory Univ Atlanta GA 30322 USA

Rare cell identification is an interesting and challenging question in flow cytometry data analysis. In the literature, manual gating is a popular approach to distill flow cytometry data and drill down to the rare cells of interest, based on prior knowledge of measured protein markers and visual inspection of the data. Several computational algorithms have been proposed for rare cell identification. To compare existing algorithms and promote new developments, FlowCAP-III put forward one computational challenge that focused on this question. The challenge provided flow cytometry data for 202 training samples and two manually gated rare cell types for each training sample, roughly 0.02 and 0.04% of the cells, respectively. In addition, flow cytometry data for 203 testing samples were provided, and participants were invited to computationally identify the rare cells in the testing samples. Accuracy of the identification results was evaluated by comparing to manual gating of the testing samples. We participated in the challenge, and developed a method that combined the Hellinger divergence, a downsampling trick and the ensemble SVM. Our method achieved the highest accuracy in the challenge. (c) 2015 International Society for Advancement of Cytometry

关键词： rare cells computational prediction manual gating FlowCAP

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