BDDCS, the Rule of 5 and drugability

https://doi.org/10.1016/j.addr.2016.05.007Get rights and content

Abstract

The Rule of 5 methodology appears to be as useful today in defining drugability as when it was proposed, but recognizing that the database that we used includes only drugs that successfully reached the market. We do not view additional criteria necessary nor did we find significant deficiencies in the four Rule of 5 criteria originally proposed by Lipinski and coworkers. BDDCS builds upon the Rule of 5 and can quite successfully predict drug disposition characteristics for drugs both meeting and not meeting Rule of 5 criteria. More recent expansions of classification systems have been proposed and do provide useful qualitative and quantitative predictions for clearance relationships. However, the broad range of applicability of BDDCS beyond just clearance predictions gives a great deal of further usefulness for the combined Rule of 5/BDDCS system.

Introduction

In their 2005 introduction of the Biopharmaceutics Drug Disposition Classification System (BDDCS) Wu and Benet [1] wrote, “New molecular entities (NMEs) today are frequently large-molecular-weight, lipophilic, poorly-water soluble compounds that most often fall into BCS Class 2. Lipinski et al. [2] pointed out that leads obtained through high-throughput screening (HTS) tend to have higher molecular weights and greater lipophilicity than leads in the pre-HTS era. Lipinski's Rule of 5 was developed to set ‘drugability’ guidelines for NMEs [3]. In the drug discovery setting, the Rule of 5 predicts that poor absorption or permeation is more likely when there are more than 5 H-bond donors, 10 H-bond acceptors, the molecular weight is greater than 500, and the calculated Log P (CLog P) is greater than 5. However, Lipinski specifically states that the Rule of 5 only holds for compounds that are not substrates for active transporters [2], [3]. When the Rule of 5 was developed, information about drug transporters was very limited. We believe that almost all drugs are substrates for some transporter. Studies to date have not been able to show this because we are just beginning to gain the knowledge and tools that allow investigation of substrates for uptake transporters. In addition, unless a drug molecule can passively gain intracellular access, it is not possible to simply investigate whether the molecule is a substrate for efflux transporters.”

Now, more than 10 years beyond that 2005 publication we do have much more information about the prevalence and relevance of transporters to drug disposition through the initial publication of the International Transporter Consortium [4] and many subsequent publications from this group and others. It is likely that all drugs are substrates for at least one transporter, but in this manuscript we discuss when transporters are likely to mediate a clinically relevant response, such that a drug's in vivo disposition depends on and reflects the functionality of transporters.

Here, we evaluate whether the concepts that underlie BDDCS are equally applicable to traditional (within Rule of 5) drugs and also to the increasing number of compounds in development that sit outside the Rule of 5. Yet, academics such as us and many of the other contributors to this compilation are limited in our ability to evaluate the relevance of “beyond Rule-of-5” advances. We suggest this is due to two major factors. First, industrial scientists such as Lipinski and his colleagues have the distinct advantage over academic scientists in that they have access to information about a multitude of candidate drugs that were not successful in achieving regulatory approval for marketing, in addition to the approved drug products that serve as the data base for investigations by academic scientists. Even so, drug companies are limited to the unsuccessful candidates in their pipeline. Thus, in an effort to more fully understand reasons for attrition, four major pharmaceutical companies have evaluated their combined datasets including all candidates and approved drugs [5]. Second, the universal acceptance of the Rule of 5 (Ro5) principles by medicinal chemists and industrial firms may have markedly changed the number of compounds with two or more Ro5 violations being pursued in drug development. It should be noted that the above observations are applicable to chemically synthesized small molecule drugs (in contrast to natural products or chemical derivatives of natural products) that are intended for oral delivery, and are not likely to be relevant to injectable small molecule drugs, nor are they relevant to other therapeutic categories such as biologics or volatile anesthetics.

Soon after the Ro5 publication, Oprea [6] showed that Ro5 criteria do not serve to discriminate drugs from “non-drugs”, i.e., approved drugs compared to molecules that are not likely to be therapeutically relevant. Over 90% of the compilation of chemical reagents known as the Available Chemicals Directory are also Ro5 compliant. This observation, however, does not negate the notion that the criteria embodied by the Ro5 can be used to narrow the properties that are useful for what could be termed the “therapeutically relevant pharmacokinetic space”.

BDDCS was not developed as an alternative or even an extension of the Ro5. Rather the purpose of BDDCS is to predict drug disposition and potential drug–drug interactions with an emphasis on defining which drugs would be amenable to enzymatic-only and transporter-only disposition and drug–drug interactions, as well as where transporter enzyme interplay may be important. However, as detailed below, BDDCS applications have extended beyond these original intentions.

Section snippets

Historical development of BDDCS

The BDDCS was an outgrowth of the Biopharmaceutics Classification System (BCS), which developed from the seminal 1995 paper of Amidon et al. [7] that led to the FDA BCS Guidance in 2000 [8]. Wu and Benet recognized that the overwhelming majority of BCS Class 1 and Class 2 drugs were eliminated in man primarily via metabolic processes, while the overwhelming majority (41 of 42) of BCS Class 3 and Class 4 drugs classified at that time were primarily eliminated in man unchanged in the urine and

Marketed drugs' characteristics

“To facilitate use of the BDDCS system for making predictions for marketed drugs, in 2011 we compiled the BDDCS classification for 927 drugs, which included 30 active metabolites, primarily the active species from prodrugs” [17]. More recently, Hosey et al. [18] incorporated an additional 175 drugs into the system and amended the classification of 11 drugs from the previous compilation. Our analysis here evaluates the more than 1100 drugs compilation as amended, where we have excluded 14 Class

Metabolism versus excretion of unchanged drug in the urine and bile as the major elimination route for an NME in humans

The major, but simple, discovery from the BDDCS was the recognition that the jejunal intestinal permeability rate could differentiate metabolism versus excretion of unchanged drug as the primary route of elimination of an NME in humans [1]. It was then shown that in vitro permeability rate measures of an NME in cellular systems and even in non-biological membranes such as PAMPA would allow this prediction to be made before the NME had ever been dosed to animals or humans. Hosey and Benet [31]

Metabolism as a biowaiver criterion

Since the BDDCS was proposed in 2005 [1], a number of extensions, providing new insights and predictions have been proposed. The excellent correlation between the high extent of absorption and the extent of metabolism led a number of experts in the field to recommend the use of BDDCS in classifying the permeability of marketed drugs using measures of the extent of metabolism following systemic absorption as justifying how much of the drug was absorbed [40]. This proposal was accepted by the EMA

Conclusions

The Ro5 methodology appears to be as useful today in defining therapeutically relevant pharmacokinetic drugability as when it was proposed, but recognizing that the database that we evaluated includes only drugs that successfully reached the market. As shown earlier, Ro5 fails to discriminate drugs from “non-drugs”. From our perspective, we do not view additional criteria to be necessary or find significant deficiencies in the four Ro5 criteria originally proposed by Lipinski and coworkers [2],

Acknowledgments

The work carried out in Dr. Benet's laboratory was supported in part by NIH grant GM61390. CMH was supported in part by NIH Training Grant T32 GM007175 and the Pharmaceutical Research and Manufacturers of America (PhRMA) Foundation Pre Doctoral Fellowship in Pharmaceutics. The work carried out by Dr. Ursu and Dr. Oprea was supported in part by NIH grant 1U54CA189205-01.

References (71)

  • C.G. Daughton

    Eco-directed sustainable prescribing: feasibility for reducing water contamination by drugs

    Sci. Total Environ.

    (2014)
  • C.-Y. Wu et al.

    Predicting drug disposition via application of BCS: transport/absorption/elimination interplay and development of a biopharmaceutics drug disposition classification system

    Pharm. Res.

    (2005)
  • The International Transporter Consortium et al.

    Membrane transporters in drug development

    Nat. Rev. Drug Discov.

    (2010)
  • G.L. Amidon et al.

    An analysis of the attrition of drug candidates from four major pharmaceutical companies

    Nat. Rev. Drug Discov.

    (2015)
  • T.I. Oprea

    Property distribution of drug-related chemical databases

    J. Comput. Aided Mol. Des.

    (2000)
  • M.J. Waring et al.

    A theoretical basis for a biopharmaceutics drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability

    Pharm. Res.

    (1995)
  • Food and Drug Administration

    Guidance for industry: waiver of in vivo bioavailability and bioequivalence studies for immediate-release solid oral dosage forms based on a biopharmaceutics classification system

    (2000)
  • V.J. Wacher et al.

    Overlapping substrate specificities and tissue distribution of cytochrome P450 3A and P-glycoprotein: implications for drug delivery and activity in cancer chemotherapy

    Mol. Carcinog.

    (1995)
  • C.-Y. Wu et al.

    Differentiation of absorption and first-pass gut and hepatic metabolism in humans: studies with cyclosporine

    Clin. Pharmacol. Ther.

    (1995)
  • C.L. Cummins et al.

    Sex-related differences in the clearance of Cytochrome P450 3 A4 substrates may be caused by P-glycoprotein

    Clin. Pharmacol. Ther.

    (2002)
  • L.Z. Benet et al.

    Transporter-enzyme interactions: implications for predicting drug–drug interactions from in vitro data

    Curr. Drug Metab.

    (2003)
  • C.-Y. Wu et al.

    Disposition of tacrolimus in isolated perfused rat liver: influence of troleandomycin, cyclosporine, and GG918

    Drug Metab. Dispos.

    (2003)
  • Y.Y. Lau et al.

    Ex situ inhibition of hepatic uptake and efflux significantly changes metabolism: hepatic enzyme-transporter interplay

    J. Pharmacol. Exp. Ther.

    (2004)
  • S. Shugarts et al.

    The role of transporters in the pharmacokinetics of orally administered drugs

    Pharm. Res.

    (2009)
  • L.Z. Benet et al.

    BDDCS applied to over 900 drugs

    AAPS J.

    (2011)
  • C.M. Hosey et al.

    BDDCS predictions, self-correcting aspects of BDDCS assignments, BDDCS assignment correction, and classification for more than 175 additional drugs

    AAPS J.

    (2016)
  • M.V. Varma et al.

    Predicting clearance mechanism in drug discovery: extended clearance classification system

    Pharm. Res.

    (2015)
  • M. Niemi et al.

    SLCO1B1 polymorphism and sex affect the pharmacokinetics of pravastatin but not fluvastatin

    Clin. Pharmacol. Ther.

    (2006)
  • A. Kalliokoski et al.

    Impact of OATP transporters on pharmacokinetics

    Br. J. Pharmacol.

    (2009)
  • C.L. Cummins et al.

    Characterizing the expression of CYP3A4 and efflux transporters (P-gp, MRP1, and MRP2) in CYP3A4-transfected Caco-2 cells after induction with sodium butyrate and the phorbol ester 12-O-tetradecanoylphorbol-13-acetate

    Pharm. Res.

    (2001)
  • C.L. Cummins et al.

    Unmasking the dynamic interplay between intestinal P-glycoprotein and CYP3A4

    J. Pharmacol. Exp. Ther.

    (2002)
  • C.L. Cummins et al.

    In vivo modulation of intestinal CYP3A metabolism by P-glycoprotein: studies using the rat single-pass intestinal perfusion model

    J. Pharmacol. Exp. Ther.

    (2003)
  • C.L. Cummins et al.

    CYP3A4-transfected Caco-2 cells as a tool for understanding biochemical absorption barriers: studies with sirolimus and midazolam

    J. Pharmacol. Exp. Ther.

    (2004)
  • F. Broccatelli et al.

    BDDCS class prediction for new molecular entities

    Mol. Pharm.

    (2012)
  • Y. Zheng et al.

    Reliability of in vitro and in vivo methods for predicting the effect of P-glycoprotein on the delivery of antidepressants to the brain

    Clin. Pharmacokinet.

    (2016)
  • Cited by (498)

    View all citing articles on Scopus

    This review is part of the Advanced Drug Delivery Reviews theme issue on “Understanding the challenges of beyond-rule-of-5 compounds”.

    View full text