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      A BOILED‐Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules

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          Abstract

          Apart from efficacy and toxicity, many drug development failures are imputable to poor pharmacokinetics and bioavailability. Gastrointestinal absorption and brain access are two pharmacokinetic behaviors crucial to estimate at various stages of the drug discovery processes. To this end, the Brain Or IntestinaL EstimateD permeation method (BOILED‐Egg) is proposed as an accurate predictive model that works by computing the lipophilicity and polarity of small molecules. Concomitant predictions for both brain and intestinal permeation are obtained from the same two physicochemical descriptors and straightforwardly translated into molecular design, owing to the speed, accuracy, conceptual simplicity and clear graphical output of the model. The BOILED‐Egg can be applied in a variety of settings, from the filtering of chemical libraries at the early steps of drug discovery, to the evaluation of drug candidates for development.

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          Most cited references13

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          Coexistence of passive and carrier-mediated processes in drug transport.

          The permeability of biological membranes is one of the most important determinants of the pharmacokinetic processes of a drug. Although it is often accepted that many drug substances are transported across biological membranes by passive transcellular diffusion, a recent hypothesis speculated that carrier-mediated mechanisms might account for the majority of membrane drug transport processes in biological systems. Based on evidence of the physicochemical characteristics and of in vitro and in vivo findings for marketed drugs, as well as results from real-life discovery and development projects, we present the view that both passive transcellular processes and carrier-mediated processes coexist and contribute to drug transport activities across biological membranes.
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            Knowledge-Based, Central Nervous System (CNS) Lead Selection and Lead Optimization for CNS Drug Discovery

            The central nervous system (CNS) is the major area that is affected by aging. Alzheimer’s disease (AD), Parkinson’s disease (PD), brain cancer, and stroke are the CNS diseases that will cost trillions of dollars for their treatment. Achievement of appropriate blood–brain barrier (BBB) penetration is often considered a significant hurdle in the CNS drug discovery process. On the other hand, BBB penetration may be a liability for many of the non-CNS drug targets, and a clear understanding of the physicochemical and structural differences between CNS and non-CNS drugs may assist both research areas. Because of the numerous and challenging issues in CNS drug discovery and the low success rates, pharmaceutical companies are beginning to deprioritize their drug discovery efforts in the CNS arena. Prompted by these challenges and to aid in the design of high-quality, efficacious CNS compounds, we analyzed the physicochemical property and the chemical structural profiles of 317 CNS and 626 non-CNS oral drugs. The conclusions derived provide an ideal property profile for lead selection and the property modification strategy during the lead optimization process. A list of substructural units that may be useful for CNS drug design was also provided here. A classification tree was also developed to differentiate between CNS drugs and non-CNS oral drugs. The combined analysis provided the following guidelines for designing high-quality CNS drugs: (i) topological molecular polar surface area of <76 Å2 (25–60 Å2), (ii) at least one (one or two, including one aliphatic amine) nitrogen, (iii) fewer than seven (two to four) linear chains outside of rings, (iv) fewer than three (zero or one) polar hydrogen atoms, (v) volume of 740–970 Å3, (vi) solvent accessible surface area of 460–580 Å2, and (vii) positive QikProp parameter CNS. The ranges within parentheses may be used during lead optimization. One violation to this proposed profile may be acceptable. The chemoinformatics approaches for graphically analyzing multiple properties efficiently are presented.
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              Discovery and development of telaprevir: an NS3-4A protease inhibitor for treating genotype 1 chronic hepatitis C virus.

              Infection with hepatitis C virus (HCV) is a major medical problem with over 170 million people infected worldwide. Substantial morbidity and mortality are associated with hepatic manifestations (cirrhosis and hepatocellular carcinoma), which develop with increasing frequency in people infected with HCV for more than 20 years. Less well known is the burden of HCV disease associated with extrahepatic manifestations (diabetes, B-cell proliferative disorders, depression, cognitive disorders, arthritis and Sjögren's syndrome). For patients infected with genotype 1 HCV, treatment with polyethylene glycol decorated interferon (peginterferon) α and ribavirin (PR) is associated with a low (40-50%) success rate, substantial treatment-limiting side effects and a long (48-week) duration of treatment. In the past 15 years, major scientific advances have enabled the development of new classes of HCV therapy, the direct-acting antiviral agents, also known as specifically targeted antiviral therapy for hepatitis C (STAT-C). In combination with PR, the HCV NS3-4A protease inhibitor telaprevir has recently been approved for treatment of genotype 1 chronic HCV in the United States, Canada, European Union and Japan. Compared with PR, telaprevir combination therapy offers significantly improved viral cure rates and the possibility of shortened treatment duration for diverse patient populations. Developers of innovative drugs have to blaze a new path with few validated sign posts to guide the way. Indeed, telaprevir's development was once put on hold because of its performance in a standard IC(50) assay. Data from new hypotheses and novel experiments were required to justify further investment and reduce risk that the drug might fail in the clinic. In addition, the poor drug-like properties of telaprevir were a formidable hurdle, which the manufacturing and formulation teams had to overcome to make the drug. Finally, novel clinical trial designs were developed to improve efficacy and shorten treatment in parallel instead of sequentially. Lessons learned from the development of telaprevir suggest that makers of innovative medicines cannot rely solely on traditional drug discovery metrics, but must develop innovative, scientifically guided pathways for success.
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                Author and article information

                Contributors
                vincent.zoete@sib.swiss
                Journal
                ChemMedChem
                ChemMedChem
                10.1002/(ISSN)1860-7187
                CMDC
                Chemmedchem
                John Wiley and Sons Inc. (Hoboken )
                1860-7179
                1860-7187
                24 May 2016
                06 June 2016
                : 11
                : 11 ( doiID: 10.1002/cmdc.v11.11 )
                : 1117-1121
                Affiliations
                [ 1 ]SIB Swiss Institute of Bioinformatics Molecular Modeling Group Quartier Sorge, Bâtiment Génopode 1015 LausanneSwitzerland
                Article
                CMDC201600182
                10.1002/cmdc.201600182
                5089604
                27218427
                52489aab-60dd-4e61-92aa-6f9fe68d990f
                © 2016 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 04 April 2016
                Page count
                Figures: 3, Tables: 0, References: 22, Pages: 5
                Funding
                Funded by: SIB Swiss Institute of Bioinformatics
                Funded by: Solidar-Immun Foundation
                Categories
                Communication
                Communications
                Custom metadata
                2.0
                cmdc201600182
                June 6, 2016
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.6 mode:remove_FC converted:01.11.2016

                Pharmaceutical chemistry
                blood–brain barrier,chemoinformatics,drug absorption,medicinal chemistry,physicochemical properties

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