What is Lipinski's Rule of 5?

Some 20 years after Lipinski and colleagues proposed a series of key characteristics required of a drug for oral bioavailability; how useful are these rules now for evaluating new drug candidates?

Drug Development

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Successful oral administration and absorption of a drug is often governed by its bioavailability or the rate and extent of drug absorption. This can be quantified as the ratio of a compound's peak concentration given orally, compared to the same dose injected directly into the bloodstream. The effectiveness of these orally administered active compounds will be influenced by processes of absorption, distribution, metabolism, and excretion (ADME). Common characteristics influencing the permeability of a compound across physiological barriers have been identified.

Predicting oral bioavailability

Determining a drug candidate’s ability to cross physiological barriers and exert pharmacological effects at its target in vitro is still the topic of much research. In 1997, Lipinski and colleagues developed four Rule of 5 criteria. These rules were developed to set out useful generalizations for the structural and physicochemical properties influencing the bioavailability of orally administered active compounds.

In a drug discovery setting, these rules define predictors of poor absorption or permeability, as a molecular weight greater than 500 Da; greater than 5 hydrogen bound donors; greater than 10 hydrogen bound acceptors, and a partition coefficient (Log P) greater than 5. However, since the development of these rules, the FDA have approved hundreds of orally administered active compounds. What’s more, the average molecular mass, as well as the threshold for hydrogen-bond acceptors, of these FDA-approved drugs has substantially increased since 1997.

In silico drug candidate screening

Since the publication of Lipinski’s rules, systems have been developed to aid in assessing the bioavailability and bioequivalence of drug candidates in vitro and in silico. In 2000 the FDA established the Biopharmaceutics Classification System (BCS). Aiming to predict the in vivo performance of drug products from in vitro measurements, the BCS categorizes drug candidates into four classes; defined by the aqueous solubility and intestinal permeability characteristics of the drug substance or substances.

Current FDA guidance recommends the use of BCS framework to substantiate in vivo bioequivalence tests of drug products with immediate release, solid orally administered dosage forms; or, suspensions designed to deliver the drug to the systemic circulation. Implications of this framework extend to the selection of candidate drugs, the prediction and elucidation of food interactions, and the possibility of in-vitro/in-vivo correlation in the dissolution testing of solid formulations.

Medical Research

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Beyond the rule of 5

Breaking one or more of Lipinski’s rules, does not mean a drug candidate cannot be effective. In fact, compounds beyond the rule of five are often better suited for investigation when targeting large and flat binding sites. What’s more, many contemporary drug candidates break these rules.

However, drug candidates beyond the rule of 5 often come with greater lipophilicity and reduced water solubility compared to traditional (within Rule of 5) drugs. As a result, these drug candidates are associated with a reduced bioavailability; requiring non-traditional delivery strategies to ensure adequate accumulation at the target site.

Understandably, the oral dosage is often the preferred method of drug delivery for many people. To improve the bioavailability of drugs beyond the rule of 5, developers use properties such as intramolecular hydrogen bonding, macrocyclization, dosage, and formulations. Modified classification systems can be used to support lead optimization processes for the successful development of drug candidates beyond the rule of 5.
Computer-assisted drug development

Computational tools can be used to identify the structural and physicochemical properties of drug candidates. They can also support identifying and validating drug targets. By conducting ligand similarity-based and structure-based virtual screening, it is possible to identify hits, leads, and drug candidates with favorable physicochemical profiles for oral absorption.

Visualizing a drug candidate's interaction with its target in a matrix can allow for the prediction of how variabilities will influence its activity. Proteochemometrics (PCM) modeling combines information on both the ligand and target, to simultaneously model the interaction spaces and predict an output variable of interest. Defined by their input data, PCM can be used to model the bioactivity of a candidate drug in different biological settings. In turn, this can allow for individualized treatment options based on a genome.
Applications in pharmacogenomics

In addition to predicting effective drug candidates, PCM models can be used to interpret how differential gene expression influences drug bioactivity. By utilizing data on target components, e.g. genomic features, PCM models are able to determine the activity of drug candidates in particular disease settings. Modeling the sensitivity of viral mutants to antiretroviral drugs, or the sensitivity of cancer cell lines, PCM can facilitate personalized therapy.

By utilizing information on the target and ligand, as well as resistance data, PCM models could help predict optimal treatment regimens for those with HIV. In combination with data from cancer cell line collections, the PCM could provide a model for predicting efficacious treatment options based on an individual’s cancer genome. However, the integration of cancer cell line collections into PCM models has yet to be fully exploited, largely due to disagreements in drug sensitivity measurements.

Predicting the “druggability" of orally administered active compounds no longer relies on observed patterns in the structural difference of compounds. In silico prediction tools, provide an effective method of evaluating drug candidates.

Sources:

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  • Christel, A.S., et al. (2016) Porter, Computational prediction of formulation strategies for beyond-rule-of-5 compounds. Advanced Drug Delivery Reviews. doi.org/10.1016/j.addr.2016.02.005.
  • Cui, Y., et al. (2021) A Bidirectional Permeability Assay for beyond Rule of 5 Compounds. Pharmaceutics. doi:10.3390/pharmaceutics13081146
  • Doak, B.C., et al. (2014). Oral Druggable Space beyond the Rule of 5: Insights from Drugs and Clinical Candidates. Chemistry & Biology. doi.org/10.1016/j.chembiol.2014.08.013.
  • Lipinski, C.A., et al. (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery. Adv Drug Deliv Rev. 23, pp. 3-25
  • Katsila, T., et al. (2016) Computational approaches in target identification and drug discovery. Comput Struct Biotechnol J. doi:10.1016/j.csbj.2016.04.004
  • Cortés-Ciriano, I., et al. (2015) Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects. Med Chem Commun, 6, pp. 24-50
  • Benet LZ. (2013) The role of BCS (biopharmaceutics classification system) and BDDCS (biopharmaceutics drug disposition classification system) in drug development. J Pharm Sci. doi:10.1002/jps.23359
  • Lennernäs, H., and Abrahamsson B. (2005) The use of biopharmaceutic classification of drugs in drug discovery and development: current status and future extension. J Pharm Pharmacol. doi: 10.1211/0022357055263.
  • The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). (2021) M9 Biopharmaceutics Classification System- Based Biowaivers [PDF]. U.S. Department of Health and Human Services Food and Drug Administration. Available at: https://www.fda.gov/media/148472/download (Accessed on 14 November 2021).

Further Reading

Last Updated: Mar 4, 2022

Eve Navin

Written by

Eve Navin

Studying Biochemistry at the University of Bristol, Eve graduated in 2019 and swiftly began a career in medical communications. During their undergraduate, Eve focused their attention on the progression of cancer; with a literature review about the tumor microenvironment (TME) and a proposal for a model to aid in measuring the migratory capacity of ovarian cancer cells.

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