Drug-like properties and the causes of poor solubility and poor permeability.

By Christopher A. Lipinski. Journal of pharmacological and toxicological methods, 2000

3,351 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Pharmacology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/21bdd9f69c858c2c614710b81bd1ae5d9b503142

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Citations in the corpus

Strategies to Address Low Drug Solubility in Discovery and Development (2013, methodology): “…is the concept of a maximum absorbable dose (MAD), originally derived by Johnson and Swindell (1996) and further applied by Curatolo (1998) and Lipinski (2000): MAD 5 Cs × kabs × SIWV × SITT ð5Þ where Cs is the solubility (mg/ml) at pH 6.5 (representing the pH of the small intestine); kabs is…”
Reaching for high-hanging fruit in drug discovery at protein–protein interfaces (2007, background): “For good oral absorption (or bioavailability), most orally active drugs are less than 500 D”
Predicting Drug Disposition via Application of BCS: Transport/Absorption/ Elimination Interplay and Development of a Biopharmaceutics Drug Disposition Classification System (2004, background): “We anticipate that the DPR will have components related to physicochemical parameters [such as Lipinski Rule of 5 characteristics and parameters related to polar surface area and/or molecular flexibility (22,23,52)], together with qualifications related to uptake (and efflux) transporters, and a par”
The impact of aromatic ring count on compound developability--are too many aromatic rings a liability in drug design? (2009, background): “Given the importance of aqueous solubility on permeability and oral absorption [1, 5 ], however, if a compound in lead optimization is known to have poor solubility (or poor absorption), reducing the number of aromatic rings in the compound is likely to be beneficial.”
Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? (2015, neutral): “649 [40]”
Advances in Oral Drug Delivery (2021, background): “In addition, the hydrogen-bonding capability of the drugs dictated by the number of hydrogen bond donors and acceptors should be less than 10 and 5, respectively (Lipinski, 2000; Avdeef, 2001).”
Developing and validating predictive decision tree models from mining chemical structural fingerprints and high–throughput screening data in PubChem (2008, methodology): “In the past, HTS data has been analyzed by various cheminformatics methods [6-17], such as cluster analysis[10], selection of structural homologs[11,12], data partitioning [13-16] etc.”
A decade of fragment-based drug design: strategic advances and lessons learned (2007, background): “Given that both of these properties directly influence oral bioavailabilit”
Critical Evaluation of Nanoparticle Tracking Analysis (NTA) by NanoSight for the Measurement of Nanoparticles and Protein Aggregates (2010, background): “Most macromolecular drugs, such as proteins, peptides, DNA and RNA, cannot be administrated via the traditional oral route of administration, due to their susceptibility to enzymatic degradation or low absorption efficiency (1,2).”
Drug Nanoparticles: Formulating Poorly Water-Soluble Compounds (2008, background): “THE SOLUBILITY CHALLENGE It is estimated that ~40% of active substances identified through combinatorial screening programs are difficult to formulate as a result of their lack of significant solubility in water (Lipper, 1999; Lipinski, 2000, 2002).”
Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes* (2005, background): “The properties of druglike molecules are well studied (10, 11 ) and cover a certain range of sizes, typically with molecular mass between 300 and 700 daltons.”