Drug-like properties and the causes of poor solubility and poor permeability
01 Jul 2000-Journal of Pharmacological and Toxicological Methods (J Pharmacol Toxicol Methods)-Vol. 44, Iss: 1, pp 235-249
TL;DR: There are currently about 10000 drug-like compounds, and true diversity does not exist in experimental combinatorial chemistry screening libraries because current ADME experimental screens are multi-mechanisms, and predictions get worse as more data accumulates.
About: This article is published in Journal of Pharmacological and Toxicological Methods.The article was published on 2000-07-01. It has received 3041 citations till now. The article focuses on the topics: ADME.
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TL;DR: Reduced molecular flexibility, as measured by the number of rotatable bonds, and low polar surface area or total hydrogen bond count are found to be important predictors of good oral bioavailability, independent of molecular weight.
Abstract: Oral bioavailability measurements in rats for over 1100 drug candidates studied at SmithKline Beecham Pharmaceuticals (now GlaxoSmithKline) have allowed us to analyze the relative importance of molecular properties considered to influence that drug property. Reduced molecular flexibility, as measured by the number of rotatable bonds, and low polar surface area or total hydrogen bond count (sum of donors and acceptors) are found to be important predictors of good oral bioavailability, independent of molecular weight. That on average both the number of rotatable bonds and polar surface area or hydrogen bond count tend to increase with molecular weight may in part explain the success of the molecular weight parameter in predicting oral bioavailability. The commonly applied molecular weight cutoff at 500 does not itself significantly separate compounds with poor oral bioavailability from those with acceptable values in this extensive data set. Our observations suggest that compounds which meet only the two cr...
5,191 citations
TL;DR: This paper has prepared a library of 727,842 molecules, each with 3D structure, using catalogs of compounds from vendors, and hopes that this database will bring virtual screening libraries to a wide community of structural biologists and medicinal chemists.
Abstract: A critical barrier to entry into structure-based virtual screening is the lack of a suitable, easy to access database of purchasable compounds. We have therefore prepared a library of 727,842 molecules, each with 3D structure, using catalogs of compounds from vendors (the size of this library continues to grow). The molecules have been assigned biologically relevant protonation states and are annotated with properties such as molecular weight, calculated LogP, and number of rotatable bonds. Each molecule in the library contains vendor and purchasing information and is ready for docking using a number of popular docking programs. Within certain limits, the molecules are prepared in multiple protonation states and multiple tautomeric forms. In one format, multiple conformations are available for the molecules. This database is available for free download (http://zinc.docking.org) in several common file formats including SMILES, mol2, 3D SDF, and DOCK flexibase format. A Web-based query tool incorporating a molecular drawing interface enables the database to be searched and browsed and subsets to be created. Users can process their own molecules by uploading them to a server. Our hope is that this database will bring virtual screening libraries to a wide community of structural biologists and medicinal chemists.
3,354 citations
TL;DR: In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings.
Abstract: The Chemscore function was implemented as a scoring function for the protein-ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, "Goldscore-CS" and "Chemscore-GS," in terms of docking accuracy, prediction of binding affinities, and speed. In the "Goldscore-CS" protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the "Chemscore-GS" protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a "clean" set of 224 protein-ligand complexes, and for two subsets of this set, one for which the ligands are "drug-like," the other for which they are "fragment-like." For "drug-like" and "fragment-like" ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. "Goldscore-CS" gives success rates of up to 81% (top-ranked GOLD solution within 2.0 A of the experimental binding mode) for the "clean list," but at the cost of long search times. For most virtual screening applications, "Chemscore-GS" seems optimal; search settings that give docking speeds of around 0.25-1.3 min/compound have success rates of about 78% for "drug-like" compounds and 85% for "fragment-like" compounds. In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1-2 min/compound, the Goldscore function predicts binding energies with a standard deviation of approximately 10.5 kJ/mol.
2,505 citations
Cites methods from "Drug-like properties and the causes..."
...Lipinski’s original analysis of orally administered drugs and drug candidates led to the well-known Rule of Five.14,15 Here, we use the simpler rules to predict bioavailability derived by Veber et al.16 Hence, in our “drug-like list,” only complexes from the “clean list” were included where the ligand has 10 or fewer rotatable bonds and a polar surface area 140 Å2....
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...Lipinski’s original analysis of orally administered drugs and drug candidates led to the well-known Rule of Five.(14,15) Here, we use the simpler rules to predict bioavailability derived by Veber et al....
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TL;DR: This work has shown that the blood-brain barrier provides the platform for CNS drug delivery programs, which should be developed in parallel with traditional CNS drug discovery efforts in the molecular neurosciences.
Abstract: The blood-brain barrier (BBB) is formed by the brain capillary endothelium and excludes from the brain ∼100% of large-molecule neurotherapeutics and more than 98% of all small-molecule drugs. Despite the importance of the BBB to the neurotherapeutics mission, the BBB receives insufficient attention in either academic neuroscience or industry programs. The combination of so little effort in developing solutions to the BBB problem, and the minimal BBB transport of the majority of all potential CNS drugs, leads predictably to the present situation in neurotherapeutics, which is that there are few effective treatments for the majority of CNS disorders. This situation can be reversed by an accelerated effort to develop a knowledge base in the fundamental transport properties of the BBB, and the molecular and cellular biology of the brain capillary endothelium. This provides the platform for CNS drug delivery programs, which should be developed in parallel with traditional CNS drug discovery efforts in the molecular neurosciences.
2,226 citations
Cites background from "Drug-like properties and the causes..."
...A review of the Comprehensive Medicinal Chemistry database shows that, of more than 7000 small-molecule drugs, only 5% treat the CNS, and these drugs only treat four disorders: depression, schizophrenia, chronic pain, and epilepsy.(6,7) There are few effective small- or large-molecule drugs for the majority of CNS disorders, with the exception of Parkinson’s disease, e....
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...In another study, only 12% of drugs were active in the CNS, but only 1% of all drugs were active in the CNS for diseases other than affective disorders.(7)...
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...CNS drug discovery programs generally are higher than drugs discovered 20 years ago.(7) This is because CNS...
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TL;DR: These studies discovered small molecules that bind with drug-like potencies to 'hotspots' on the contact surfaces involved in protein–protein interactions, and bind with much higher efficiencies than do the contact atoms of the natural protein partner.
Abstract: Targeting the interfaces between proteins has huge therapeutic potential, but discovering small-molecule drugs that disrupt protein-protein interactions is an enormous challenge. Several recent success stories, however, indicate that protein-protein interfaces might be more tractable than has been thought. These studies discovered small molecules that bind with drug-like potencies to 'hotspots' on the contact surfaces involved in protein-protein interactions. Remarkably, these small molecules bind deeper within the contact surface of the target protein, and bind with much higher efficiencies, than do the contact atoms of the natural protein partner. Some of these small molecules are now making their way through clinical trials, so this high-hanging fruit might not be far out of reach.
1,801 citations
Cites background from "Drug-like properties and the causes..."
...For good oral absorption (or bioavailability), most orally active drugs are less than 500 D...
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References
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TL;DR: Experimental and computational approaches to estimate solubility and permeability in discovery and development settings are described in this article, where the rule of 5 is used to predict poor absorption or permeability when there are more than 5 H-bond donors, 10 Hbond acceptors, and the calculated Log P (CLogP) is greater than 5 (or MlogP > 415).
14,026 citations
TL;DR: The biotech industry is establishing itself as the discovery arm of the pharmaceutical industry, and in bridging the gap between academia and large pharmaceutical companies, the biotech firms have been effective instruments of technology transfer.
Abstract: Driven by chemistry but increasingly guided by pharmacology and the clinical sciences, drug research has contributed more to the progress of medicine during the past century than any other scientific factor. The advent of molecular biology and, in particular, of genomic sciences is having a deep impact on drug discovery. Recombinant proteins and monoclonal antibodies have greatly enriched our therapeutic armamentarium. Genome sciences, combined with bioinformatic tools, allow us to dissect the genetic basis of multifactorial diseases and to determine the most suitable points of attack for future medicines, thereby increasing the number of treatment options. The dramatic increase in the complexity of drug research is enforcing changes in the institutional basis of this interdisciplinary endeavor. The biotech industry is establishing itself as the discovery arm of the pharmaceutical industry. In bridging the gap between academia and large pharmaceutical companies, the biotech firms have been effective instruments of technology transfer.
2,551 citations
"Drug-like properties and the causes..." refers background in this paper
...For example, the estimated number of drug targets is only about 500 (Drews, 2000)....
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...Drugs and their targets are sparsely distributed through chemistry space (Drews, 2000), and the members of a structural chemotype can be thought of as small tight clusters in the vastness of chemistry space....
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TL;DR: It is shown that consideration of only a single conformer when computing PSA gives an excellent correlation with intestinal absorption data-as good as previously reported methods employing multiple conformers.
545 citations
"Drug-like properties and the causes..." refers background in this paper
...Absorption, distribution, metabolism, and excretion (ADME) and chemical reactivity-related toxicity is low, while biological receptor activity is higher dimensional in chemistry space, and this is partly explainable by evolutionary pressures on ADME to deal with endobiotics and exobiotics....
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...The result is that simple filters and rules work for ADME (Clark, 1999; Lewis, 2000; Lipinski, Lombardo, Dominy, & Feeney, 1997), but not Journal of Pharmacological and Toxicological Methods 44 (2000) 235± 249...
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...ADME is hard to predict for large data sets because current ADME experimental screens are multi-mechanisms, and predictions get worse as more data accumulates....
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...Keywords Chemical diversity; Drug-like; ADME; Absorption; Permeability; Solubility; Clinical candidate; Merck; Pfizer Choose an option to locate/access this article: Recommended articles Citing articles (0) Copyright © 2001 Elsevier Science Inc....
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TL;DR: A Bayesian neural network is used to distinguish between drugs and nondrugs and it is proposed to use the models to design combinatorial libraries to supplement standard diversity approaches to library design.
Abstract: We have used a Bayesian neural network to distinguish between drugs and nondrugs. For this purpose, the CMC acts as a surrogate for drug-like molecules while the ACD is a surrogate for nondrug-like molecules. This task is performed by using two different set of 1D and 2D parameters. The 1D parameters contain information about the entire molecule like the molecular weight and the the 2D parameters contain information about specific functional groups within the molecule. Our best results predict correctly on over 90% of the compounds in the CMC while classifying about 10% of the molecules in the ACD as drug-like. Excellent generalization ability is shown by the models in that roughly 80% of the molecules in the MDDR are classified as drug-like. We propose to use the models to design combinatorial libraries. In a computer experiment on generating a drug-like library of size 100 from a set of 10 000 molecules we obtain at least a 3 or 4 order of magnitude improvement over random methods. The neighborhoods def...
462 citations
"Drug-like properties and the causes..." refers background in this paper
...Filters for selecting drug-like compounds (Brennan, 2000), or schemes for differentiating between nondrug and drug-like compounds are based on analysis of libraries of a few thousand to 50000 compounds (Sadowski & Kubinyi, 1998; Shah et al., 1998)....
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TL;DR: A scoring scheme for the rapid and automatic classification of molecules into drugs and nondrugs was developed and revealed features in the molecular descriptors that either qualify or disqualify a molecule for being a drug.
Abstract: A scoring scheme for the rapid and automatic classification of molecules into drugs and nondrugs was developed. The method is a valuable new tool that can aid in the selection and prioritization of compounds from large compound collections for purchase or biological testing and that can replace a considerable amount of laborious manual work by a more unbiased approach. It is based on the extraction of knowledge from large databases of drugs and nondrugs. The method was set up by using atom type descriptors for encoding the molecular structures and by training a feedforward neural network for classifying the molecules. It was parametrized and validated by using large databases of drugs and nondrugs (169 331 molecules from the Available Chemicals Directory, ACD, and 38 416 molecules from the World Drug Index, WDI). The method revealed features in the molecular descriptors that either qualify or disqualify a molecule for being a drug and classified 83% of the ACD and 77% of the WDI adequately.
374 citations