Advanced Drug Delivery Reviews 23 (1997) 3-25
`
`
`
`_
`— odvonced V
`drug delivery
`reviews
`
`Experimental and computational approaches to estimate solubility and
`permeability in drug discovery and development settings
`
`Christopher A. Lipinski*, Franco Lombardo, Beryl W. Dominy, Paul J. Feeney
`(."ynrr(rl RP.\'(’lJl'(.'h H/l)ivI.s'ini1, Rfi:,wr In('.. Gmrrm. CT 06340, USA
`
`Received 9 August
`
`i996; accepted I4 August 1996
`
`
`
`Abstract
`
`Experimental and computational approaches to estimate solubility and permeability in discovery and development settings
`are described. ln the 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 l-I-bond acceptors, the molecular weight (MWT) is greater than 500 and the calculated
`Log P (CLogP) is greater than 5 (or MlogP>4.l5).‘Computational methodology for the rule—based Moriguchi Log P
`(MLogP) calculation is described. Turbidimetric solubility measurement is described and applied to known drugs. High
`throughput screening (HTS) leads tend to have higher MWT and Log P and lower turbidimetric solubility than leads in the
`pre—HTS era. In the development setting, solubility calculations focus on exact value prediction and are difficult because of
`polymorphism. Recent work on linear free energy relationships and Log P approaches are critically reviewed. Useful
`predictions are possible in closely related analog series when coupled with experimental
`thermodynamic solubility
`measurements.
`
`Kevw=(Jrds: Rule of 5; Computational alert; Poor absorption or permeation; MWT; MLogP; H—Bond donors and acceptors;
`Turbidimetric solubility; Thermodynamic solubility: Solubility calculation
`
`
`Contents
`
`4
`4
`4
`5
`o
`7
`7
`8
`9
`9
`l()
`10
`l()
`12
`l3
`l4
`
`‘
`
`Introduction .......................................................................................................................................................................... ..
`I.
`..
`,
`................... ..
`2. The drug discovery setting ...... ..
`
`2.l. Changes in drug leads and physico-chemical properties
`2.2. Factors affecting pbysico—chemical lead profiles ........................................................................................... ..
`2.3.
`identifying a library with favorable physico~chemical properties ................................................................. ..
`2.4. The target audience — medicinal chemists ............................. ..
`.
`1.5. Calculated properties of the ‘USAN’ library.
`2.6. The ‘rule of 5‘ and its implementation ............................................................................................................................. ..
`2.7. Orally active drugs outside the ‘rule of S’ mnemonic and biologic transporters ........................................................ ..
`2.8. High MWT USANs and the trend in MLogP ......................................................................................................... ..
`1.9. New chemical entities. calculations ................................... ..
`2.10. Drugs in absorption and permeability studies. calculations ................................................................................. ..
`2.1 l. Validating the computational alen ............................................................................................................ ..
`‘2.l2. Changes in calculated physical property profiles at Pfizer ........................................................................................ ..
`
`2.13. The rationale for measuring drug solubility in a discovery setting ............................................................................ ..
`2.14. Drugs have high turbidimetric solubility ........................................................................................................................ ..
`
`
`
`
`
`*Corresponding author. Tel: +l 860 44l356l; e-mail: LIPlNSKl@PFlZER.COM.
`
`()|69—409X/97/$32.00 Copyright © 1997 Elsevier Science B.\/. All rights reserved
`PII Slj)l69~409X(96)00423—l
`
`AuRo— EXHIBIT 1017
`
`

`
`4
`
`CA. Lt)>inski eta/. I Adronced Drug De!ii'<'IT Reriell',\ 23 ( !lJlJ7) 3-.75
`
`2.15. High throughput sueening hits. calculations and solubility measurements .
`2.16. The triad of potency. solubility and permeability.
`.. ............ ..
`2.17. Protocols for measuring drug solubility in a discovery setting....
`.. ............ .
`2.1 H. Technical considerations and signal processing
`3. Calculation of absorption parameters
`3.1. Overall approach ...
`3.2. MLogP. Log P by the method of Moriguchi .
`3.3. MLogP calculations
`4. The development setting: prediction of aqueous thermodynamic solubility
`4.1. General considerations .
`4.2. LSERs and TLSER methods ......
`4.3. LogP and AQlJAFAC methods ..
`4.4. Other calculation methods
`5. Conclusion .
`References .
`
`14
`15
`15
`16
`17
`17
`17
`IX
`IX
`IX
`19
`21
`22
`23
`24
`
`1. Introduction
`
`2. The drug discovery setting
`
`This review presents distinctly different but com(cid:173)
`plementary experimental and computational ap(cid:173)
`proaches to estimate solubility and permeability in
`drug discovery and drug development settings. In the
`discovery setting, we describe an experimental ap(cid:173)
`proach to turbidimetric solubility measurement as
`well as computational approaches to absorption and
`permeability. The absence of discovery experimental
`approaches to permeation measurements reflects the
`authors' experience at Pfizer Central Research. Ac(cid:173)
`cordingly, the balance of poor solubility and poor
`permeation as a cause of absorption problems may
`be significantly different at other drug discovery
`locations, especially if chemistry focuses on peptidic(cid:173)
`like compounds. This review deals only with solu(cid:173)
`bility and permeability as barriers to absorption.
`Intestinal wall active transporters and intestinal wall
`metabolic events that influence the measurement of
`drug bioavailability are beyond the scope of this
`review. We hope to spark lively debate with our
`hypothesis that changes in recent years in medicinal
`the
`chemistry physical property profiles may be
`result of leads generated through high throughput
`screening. In the development setting. computational
`approaches to estimate solubility are critically re(cid:173)
`viewed based on current computational solubility
`research and experimental solubility measurements.
`
`2.1. Changes in drug leads and physico-chemical
`properties
`
`In recent years, the sources of drug leads in the
`pharmaceutical industry have changed significantly.
`From about 1970 on, what were considered at that
`time to be large empirically-based screening pro(cid:173)
`grams became less and less important in the drug
`industry as the knowledge base grew for rational
`drug design [II. Leads in this era were discovered
`using both in vitro and primary in vivo screening
`assays and came from sources other than massive
`primary in vitro screens. Lead sources were varied
`coming from natural products; clinical observations
`of drug side effects [I]; published unexamined
`patents; presentations and posters at scientific meet(cid:173)
`ings; published reports in scientific journals and
`collaborations with academic investigators. Most of
`these lead sources had the common theme that the
`·chemical lead' already had undergone considerable
`scientific investigation prior to being identified as a
`drug lead. From a physical property viewpoint, the
`most poorly behaved compounds in an analogue
`series were eliminated and most often the starting
`lead was in a range of physical properties consistent
`with the previous historical record of discovering
`orally active compounds.
`
`

`
`C.A. Lipinski eta/. I Advanced Drug Deliven Rn·int·s 23 ( /997) 3-25
`
`5
`
`This situation changed dramatically about 1989-
`199 I. Prior to 1989, it was technically unfeasible to
`in vitro activity across hundreds of
`screen for
`thousands of compounds, the volume of random
`screening required to efficiently discover new leads.
`With the advent of high throughput screening in the
`1989-1991 time period, it became technically feas(cid:173)
`ible to screen hundreds of thousands of compounds
`across in vitro assays [2-4]. Combinatorial chemis(cid:173)
`try soon began 1 and allowed automated synthesis of
`massive numbers of compounds for screening in the
`new HTS screens. The process was accelerated by
`the rapid progress in molecular genetics which made
`possible the expression of animal and human re(cid:173)
`ceptor subtypes in cells lacking receptors that might
`interfere with an assay and by the construction of
`receptor constructs to facilitate signal detection. The
`large numbers of compounds
`screening of very
`necessitated a radical departure from the traditional
`method of drug solubilization. Compounds were no
`longer solubilized in aqueous media under thermo(cid:173)
`dynamic equilibrating conditions. Rather, compounds
`were dissolved in dimethyl sulfoxide (DMSO) as
`stock solutions, typically at about 20-30 mmol and
`then were serially diluted into 96-well plates for
`assays (perhaps with some non ionic surfactant to
`improve solubility). In this paradigm, even very
`insoluble dmgs could be tested because the kinetics
`of compound crystallization determined the apparent
`'solubility' level. Moreover, compounds could parti(cid:173)
`tion
`into assay components such as membrane
`particulate material or cells or could bind to protein
`attached to the walls of the wells in the assay plate.
`The net effect was a screening technology for
`compounds in the f.LM concentration range that was
`largely divorced from the compounds true aqueous
`thermodynamic solubility. The apparent 'solubility'
`in the HTS screen is always higher, sometimes
`dramatically so, than the true thermodynamic solu(cid:173)
`bility achieved by equilibration of a well character(cid:173)
`ized solid with aqueous media. The in vitro HTS
`testing process is quite reproducible and potential
`problems related to poor compound solubility are
`
`'A search through Sci Search and Chemical Abstracts for refer·
`ences to combinatorial chemistry in titles or descriptors using the
`truncated terms COMBIN? and CHEMISTR'' gave the following
`number of references respectively: 1990, 0 and 0; 1991, 2 and I;
`1993. 8 and 8; 1994, 12 and 11; 1995. 46 and 45.
`
`the
`the follow-up to
`often compensated for by
`primary screen. This is typically a more careful,
`more labor-intensive process of in vitro retesting to
`determine IC50s from dose response curves with
`more attention paid to solubilization. The net result
`of all these testing changes is that in vitro activity is
`reliably detected
`in compounds with very poor
`thermodynamic solubility properties. A corollary
`result is that the measurement of the true thermo(cid:173)
`dynamic aqueous solubility is not very relevant to
`the screening manner in which leads are detected.
`
`2.2. Factors affecting physico-chemical lead
`pn~files
`
`The physico-chemical profile of current leads i.e.
`the 'hits' in HTS screens now no longer depends on
`compound solubility sufficient for in vivo activity
`but depends on: ( 1) the medicinal chemistry princi(cid:173)
`ples relating structure to in vitro activity; (2) the
`nature of the HTS screen; (3) the physico-chemical
`profile of the compound set being screened and ( 4)
`to human decision making, both overt and hidden as
`to the acceptability of compounds as starting points
`for medicinal chemistry structure activity relation(cid:173)
`ship (SAR) studies.
`One of the most reliable methods in medicinal
`chemistry to improve in vitro activity is to incorpo(cid:173)
`rate properly positioned lipophilic groups. For exam(cid:173)
`ple, addition of a single methyl group that can
`'pocket' improves binding by
`occupy a receptor
`about 0.7 kcal/mol [6]. By way of contrast, it is
`generally difficult to improve in vitro potency by
`manipulation of the polar groups that are involved in
`ionic receptor interactions. The interaction of a polar
`group in a drug with solvent versus interaction with
`the target receptor is a 'wash' unless positioning of
`the polar group in the drug is precise. The traditional
`lore is that the lead has the polar groups in the
`correct (or almost correct) position and that in vitro
`potency is improved by correctly positioned lipo(cid:173)
`philic groups that occupy receptor pockets. Polar
`groups in the drug that are not required for binding
`can be tolerated if they occupy solvent space but
`they do not add to receptor binding. The net effect of
`these simple medicinal chemistry principles is that.
`other factors being equal, compounds with correctly
`positioned polar functionality will be more readily
`
`

`
`C.A. Lipinski l'f of. I Ad\'ilnced DruR Delil·err RevieW\' 13 ( 1997) 3-15
`
`detectable in HTS screens if they are larger and more
`lipophilic.
`The nature of the screen determines the physico(cid:173)
`chemical profile of the resultant 'hits'. The larger the
`the
`number of hits that are detected. the more
`physico-chemical profile of the 'hits' resembles the
`overall compound set being screened. Technical
`factors such as the design of the screen and human
`cultural factors such as the stringency of the evalua(cid:173)
`tion as to what is a suitable lead worth are major
`determinants of the physico-chemical profiles of the
`eventual
`leads. Screens designed with very high
`specificity. for example many receptor based assays.
`generate small numbers of hits in the j.LM range. In
`these types of screens the signal is easy to detect
`against background noise. the hits are few or can be
`made few by altering potency criteria and
`the
`physico-chemical profiles tend towards more lipo(cid:173)
`philic, larger, less soluble compounds. Tight control
`of the criteria for activity detection in the initial HTS
`screen minimizes labor-intensive secondary evalua(cid:173)
`tion and minimizes the effect of human biases. The
`that
`lower potency hits with more
`downside is
`favorable physico-chemical property profiles may be
`discarded.
`Cell-based assays, by their very nature tend to
`produce more
`'hits'
`than receptor-based screens.
`These types of assays monitor a functional event. for
`example a change in the level of a signaling inter(cid:173)
`mediate or the expression
`level of M-RNA or
`protein. Multiple mechanisms may lead to the mea(cid:173)
`sured end point and only a few of these mechanisms
`may be desirable. This leads to a larger number of
`hits and therefore their physico-chemical profile will
`more closely resemble that of the compound set
`importantly. a
`being screened. Perhaps, equally
`larger volume of secondary evaluation allows for a
`greater expression of human bias. Bias is especially
`difficult to quantify in the chemists perception of a
`desirable lead structure.
`The physico-chemical profile of the compound set
`being screened is the first filter
`in the physico(cid:173)
`'hit'. Obviously high
`chemical profile of an HTS
`molecular weight, high lipophilicity compounds will
`not be detected by a screen if they are not present in
`the library. In the real world, trade-offs occur in the
`choice of profiles for compound sets. An exclusively
`low molecular weight, low lipophilicity library likely
`increases the difficulty of detecting 'hits' but sim-
`
`plifies the process of discovering an orally active
`drug once the lead is identified. The converse is true
`of a high molecular weight high lipophilicity library.
`In our experience, commercially available (non
`combinatorial) compounds like those available from
`chemical supply houses tend towards lower molecu(cid:173)
`lar weights and lipophilicities.
`Human decision making, both overt and hidden
`can play a large part in the profile of HTS 'hits'. For
`example. a requirement that 'hits· possess an accept(cid:173)
`able range of measured or calculated physico-chemi(cid:173)
`cal properties will obviously affect
`the starting
`compound profiles for medicinal chemistry SAR.
`Less obvious are hidden biases. Are the criteria for a
`'hit' changing to higher potency (lower IC50) as the
`HTS screen runs? Labor-intensive secondary follow(cid:173)
`up is decreased but less potent, perhaps physico(cid:173)
`chemically more attractive leads. may be eliminated.
`How do chemists react to potential lead structures'?
`In an interesting experiment, we presented a panel of
`our most experienced medicinal chemists with a
`group of theoretical lead structures -
`all containing
`literature 'toxic· moieties. Our chemists split into
`two very divergent groups; those who saw the toxi<.:
`moieties as a bar to lead pursuit and those who
`recognized the toxic moiety but thought they might
`be able to replace the offending moiety. An easy way
`to illustrate the complexity of the chemists percep(cid:173)
`tion of lead attractiveness is to examine the re(cid:173)
`markably diverse structures of the new chemical
`entities ( NCEs) introduced to market that appear at
`the back of recent volumes of Annual Reports in
`Medicinal Chemistrv. No single pharmaceutical com(cid:173)
`pany can conduct research in all therapeutic areas
`and so some of these compounds, which are all
`marketed drugs, will inevitably be less familiar and
`potentially less desirable to the medicinal chemist at
`one research
`location. but may be familiar and
`desirable to a chemist at another research site.
`
`2.3. Identi(vinf{ a library with j(Jvorable physico(cid:173)
`chemical properties
`
`The idea in selecting a library with good absorp(cid:173)
`tion properties is to use the clinical Phase II selection
`process as a filter. Drug development is expensive
`and the most poorly behaved compounds are weeded
`out early. Our hypothesis was that poorer physico(cid:173)
`chemical properties would predominate in the many
`
`

`
`C.A. Lipinski et a/. I Advanced Drug Deliven· Reviews 23 ( /997) 3-25
`
`7
`
`compounds that enter into and fail to survive pre(cid:173)
`clinical stages and Phase I safety evaluation. We
`expected that the most insoluble and poorly perme(cid:173)
`able compounds would have been eliminated in those
`compounds that survived to enter Phase II efficacy
`studies. We could use the presence of United States
`Adopted Name (USAN) or International Non-pro(cid:173)
`prietary Name (INN) names to identify compounds
`entering Phase II since most drug companies (includ(cid:173)
`ing Pfizer) apply for these names at entry to Phase II.
`The (WDI) World Drug Index is a very large
`computerized database of about 50 000 drugs from
`the Derwent Co. The process used to select a subset
`of 2245 compounds from this database that are likely
`to have superior physico-chemical properties is as
`follows: From the 50 427 compounds in the WDI
`File. 7894 with a data field for a USAN name were
`selected as were 6320 with a data field for an INN.
`From the two lists, 8548 compounds had one or both
`USAN or INN names. These were searched for a
`data field 'indications and usage' suggesting clinical
`exposure, resulting in 3704 entries. From the 3704
`using a substructure data field we eliminated 1176
`compounds with the text string 'POLY', 87 with the
`text string 'PEPTIDE' and 101 with the text string
`'QUAT'. Also eliminated were 53 compounds con(cid:173)
`taining the fragment 0 = P-0. We coined the term
`'USAN' library for this collection of drugs.
`
`2.4. The target audience - medicinal chemists
`
`Having identified a library of drugs selected by the
`economics of entry to the Phase II process we sought
`to identify calculable parameters for that library that
`were likely related to absorption or permeability. Our
`approach and choice of parameters was dictated by
`very pragmatic considerations. We wanted to set up
`an absorption-permeability alert procedure to guide
`our medicinal chemists. Keeping in mind our target
`audience of organic chemists we wanted to focus on
`the chemists very strong pattern recognition and
`chemical structure recognition skills. If our target
`audience had been pharmaceutical scientists we
`would not have deliberately excluded equations or
`regression coefficients. Experience had taught us that
`a focus on the chemists very strong skills in pattern
`recognition and their outstanding chemistry structural
`recognition skills was likely to enhance information
`transfer. In effect. we deliberately emphasized en-
`
`hanced educational effectiveness towards a well
`defined target audience at the expense of a loss of
`detail. Tailoring the message to the audience is a
`basic communications principle. One has only to
`look at the popular chemistry abstracting booklets
`with their page after page of chemistry structures and
`minimal text to appreciate the chemists structural
`recognition skills. We believe that our chemists have
`accepted our calculations at least in part because the
`calculated parameters are very readily visualized
`structurally and are presented in a pattern recognition
`format.
`
`2.5. Calculated properties of' the 'USAN · library
`
`Molecular weight (formula weight in the case of a
`salt) is an obvious choice because of the literature
`relating poorer intestinal and blood brain barrier
`permeability to increasing molecular weight [7,8]
`and the more rapid decline in permeation time as a
`function of molecular weight in lipid hi-layers as
`opposed
`to aqueous media [9]. The molecular
`weights of compounds in the 2245 USANs were
`lower than those in the whole 50 427 WDI data set.
`In the USAN set 11% had MWTs > 500 compared to
`22% in the entire data set. Compounds with MWT >
`600 were present at 8% in the USAN set compared
`to 14% in the entire data set. This difference is not
`explainable by the elimination of the very high
`MWTs in the USAN selection process. Rather it
`reflects the fact that higher MWT compounds are in
`general less likely to be orally active than lower
`MWTs.
`Lipophilicity expressed as a ratio of octanol
`solubility to aqueous solubility appears in some form
`in almost every analysis of physico-chemical prop(cid:173)
`erties related to absorption [I 0]. The computational
`problem is that an operationally useful computational
`alert to possible absorption-permeability problems
`must have a no fail
`log P calculation. In our
`experience, the widely used and accurate Pomona
`College Medicinal Chemistry program applied to our
`compound file failed to provide a calculated log P
`(CLogP) value because of missing fragments for at
`least 25% of compounds. The problem is not an
`in our
`inordinate number of 'strange fragments'
`chemistry libraries but rather lies in the direction of
`the trade off between accuracy and ability to calcu(cid:173)
`late all compounds adopted by the Pomona College
`
`

`
`8
`
`C.A. Lipinski el a/. I Adl'i.lllced l>rug Deliver\' Rnii'<~'S 23 ( 1997) 3-25
`
`team. The CLogP calculation emphasizes high ac(cid:173)
`curacy over breadth of calculation coverage. The
`fragmental CLogP value is defined with reference to
`five types of intervening isolating carbons between
`the polar fragments. As common a polar fragment as
`a sulfide ( -S-) linkage generates missing fragments
`when flanked by rare combinations of the isolating
`carbon types. Polar fragments as defined by the
`CLogP calculation can be very large and are not
`calculated as the sum of smaller, more common,
`polar fragments. This approach enhances accuracy
`but increases the number of missing fragments.
`We implemented the log P calculation (MLogP) as
`described by Moriguchi et al. [ Ill within the Molec(cid:173)
`ular Design Limited MACCS and ISIS base pro(cid:173)
`grams to avoid the missing fragment problem. A~ a
`rule-based system, the Moriguchi calculation always
`gives an answer. The pros and cons of the Moriguchi
`algorithm have been debated in the literature [ 12. I] 1.
`We
`recommend
`that, within analog series, our
`medicinal chemists use the more accurate Pomona
`CLogP calculation if possible. For calculation or
`tracking of library properties
`the
`less accurate
`MLogP program is used.
`Only about 10% of USAN compounds have a
`CLogP over 5. The CLogP value of 5 calculated on
`the USAN data set corresponds to an MLogP of
`4.15. The slope of CLogP (x axis) versus MLogP (r
`axis) is less than unity. At the high log P end, the
`Moriguchi MLogP
`is somewhat
`lower than
`the
`MedChem CLogP. In the middle log P range at about
`2. the two scales are similar. Experimentally there is
`almost certainly a lower (hydrophilic) log P limit to
`absorption and permeation. Operationally, we have
`ignored a lower limit because of the errors in the
`MLogP calculation and because excessively hydro(cid:173)
`philic compounds are not a problem in compounds
`originating in our medicinal chemistry laboratories.
`An excessive number of hydrogen bond donor
`groups impairs permeability across a membrane bi(cid:173)
`layer [14, 15[. Hydrogen donor ability can be mea(cid:173)
`sured indirectly by the partition coefficient between
`strongly hydrogen bonding solvents like water or
`ethylene glycol and a non hydrogen bond accepting
`solvent like a hydrocarbon 115] or as the log of the
`ratio of octanol to hydrocarbon partitioning. In vitro
`systems for studying intestinal drug absorption have
`been recently reviewed 1161. Computationally, hy(cid:173)
`drogen donor ability differences can be expressed by
`the solvatochromic a parameter of a donor group
`
`with perhaps a steric modifier to allow for the
`interactions between donor and acceptor moieties.
`Experimental a values for hydrogen bond donors and
`13 values for acceptor groups [ 17 j have been com(cid:173)
`piled by Professor Abraham in the UK and by the
`Raevsky group in Russia f 18, 19]. Both research
`groups currently express the hydrogen bond donor
`and acceptor properties of a moiety on a thermo(cid:173)
`dynamic free energy scale. In the Raevsky C scale,
`donors range from about - 4.0 for a very strong
`donor to
`- 0.5 for a very weak donor. Acceptors
`values in the Raevsky C scale are all positive and
`range from about 4.0 for a strong acceptor to about
`0.5 for a weak acceptor. In the Abraham scale both
`donors and acceptors have positive values that are
`about one-quarter of the absolute C values in the
`Raevsky scale.
`We found that simply adding the number of NH
`bonds and OH bonds does remarkably well as an
`index of H bond donor character. Importantly, this pa(cid:173)
`rameter has direct structural relevance to the chemist.
`When one looks at the USAN library there is a sharp
`cutoff in the number of compounds containing more
`than 5 OHs and NHs. Only 8% have more than 5. So
`92% of compounds have five or fewer H bond donors
`and it is the smaller number of donors that the litera(cid:173)
`ture links with better permeability.
`Too many hydrogen bond acceptor groups also
`hinder permeability across a membrane bi-layer. The
`sum of Ns and Os is a rough measure of H bond
`accepting ability. This very simple calculation is not
`nearly as good as the OH and NH count (as a model
`for donor ability) because there is far more variation
`in hydrogen bond acceptor than donor ability across
`atom types. For example, a pyrrole and pyridine
`nitrogen count equally as acceptors in the simple N
`0 sum calculation even though a pyridine nitrogen is
`a very good acceptor (2.72 on the C scale) and the
`pyrrole nitrogen is an far poorer acceptor ( 1.]] on
`the C scale). The more accurate solvatochromic 13
`parameter which measures acceptor ability varies far
`more on a per nitrogen or oxygen atom basis than the
`corresponding a parameter. When we examined the
`USAN library we found a fairly sharp cutoff in
`profiles with only about 12% of compounds having
`more than I 0 Ns and Os.
`
`2.6. The 'rule of 5' and its implementation
`
`At this point we had four parameters that we
`
`

`
`C.A. Lipinski et a/. I Advanced Drug Delivery Rel'iews 23 ( /997) 3-25
`
`9
`
`thought should be globally associated with solubility
`and permeability; namely molecular weight; Log P;
`the number of H-bond donors and the number of
`H-bond acceptors. In a manner similar to setting the
`oo~~~k~~mu~~OOm~%~u~
`how these four parameters needed to be set so that
`about 90% of the USAN compounds had parameters
`in a calculated range associated with better solubility
`or permeability. This analysis
`led to a simple
`'rule of 5' [20]
`mnemonic which we called the
`because the cutoffs for each of the four parameters
`were all close to 5 or a multiple of 5. In the USAN
`set we found that the sum of Ns and Os in the
`molecular formula was greater than 10 in 12% of the
`compounds. Eleven percent of compounds had a
`MWT of over 500. Ten percent of compounds had a
`CLogP larger than 5 (man MLogP larger than 4.15)
`and in 8% of compounds the sum of OHs and NHs
`in the chemical structure was larger than 5. The 'rule
`of 5' states that: poor absorption or permeation are
`more likely when:
`
`There are more than 5 H-bond donms (expressed
`as the sum of OHs and NHs);
`The MWT is over 500;
`The Log Pis over 5 (or MLogP is over 4.15);
`There are more than 10 H-bond acceptors (ex(cid:173)
`pressed as the sum of Ns and Os)
`Compound classes that are substrates for bio(cid:173)
`logical transporters are exceptions to the rule.
`
`When we examined combinations of any two of
`the four parameters in the USAN data set, we found
`that combinations of two parameters outside the
`desirable range did not exceed 10%. The exact
`values from the USAN set are: sum of N and
`0 +sum of NH and OH -
`10%; sum of N and
`0 + MWT -
`7%; sum of NH and OH + MWT -
`4% and sum of MWT + Log P -
`I%. The rarity
`(I%) among USAN drugs of the combination of
`high MWT and high log P was striking because this
`particular combination of physico-chemical proper-
`
`ties in the USAN list is enhanced in the leads
`resulting from high throughput screening.
`The rule of 5 is now implemented in our registra(cid:173)
`tion system for new compounds synthesized in our
`medicinal chemistry laboratories and the calculation
`program runs automatically as the chemist registers a
`new compound. If two parameters are out of range, a
`'poor absorption or permeability is possible' alert
`appears on the registration screen. All new com(cid:173)
`pounds are registered and so the alert is a very
`visible educational tool for the chemist and serves as
`a tracking tool for the research organization. No
`chemist is prevented from registering a compound
`because of the alert calculation.
`
`2. 7. Orally active drugs outside the 'rule of 5'
`mnemonic and biologic transporters
`
`'rule of 5' is based on a distribution of
`The
`calculated properties among several thousand drugs.
`Therefore by definition, some drugs will lie outside
`the parameter cutoffs in the rule. Interestingly, only a
`small number of therapeutic categories account fm
`most of the USAN drugs with properties falling
`outside our parameter cutoffs. These orally active
`therapeutic classes outside
`the
`'rule of 5' are:
`antibiotics, antifungals, vitamins and cardiac glyco(cid:173)
`sides. We suggest that these few therapeutic classes
`contain orally active drugs that violate the 'rule of 5'
`because members of these classes have structural
`features that allow the drugs to act as substrates for
`naturally occurring transporters. When the 'rule of 5'
`is modified to exclude these few drug categories only
`a very few exceptions can be found. For example.
`among the NCEs between 1990 and 1993 falling
`outside the double cutoffs in 'the rule of 5', there
`were nine non-orally active drugs and the only orally
`active compounds outside the double cutoffs were
`seven antibiotics. Fungicides-protoazocides-antisep(cid:173)
`tics also fall outside the rule. For example, among
`the 41 USAN drugs with MWT > 500 and MLogP >
`4.15 there were nine drugs in this class. Vitamins are
`another orally active class drug with parameter
`values outside the double cutoffs. Close to I 00
`vitamins fell into this category. Cardiac glycosides.
`an orally active drug class also fall outside the
`parameter limits of the rule of 5. For example among
`90 USANs with high MWT and low MLogP there
`were two cardiac glycosides.
`
`

`
`10
`
`C.A.. Lipinski et a/. I Admnced Drug De/irerl" RerieH·s ]3 ( /997) 3-]5
`
`2.8. High MWT USANs and the trend in MLogP
`
`In our USAN data set we plotted MLogP against
`MWT and examined the compound distributions as
`defined by the 50 and 90% probability ellipses. A
`large number of USAN compounds had MLogP
`more negative than - 0.5. Among the USAN com(cid:173)
`pounds there was a trend for higher MWT to
`correlate with lower MLogP. This type of trend is
`distinctly different from
`the positive correlation
`between MLogP and MWT found in most SAR data
`sets. Usually as MWT increases, compound lipo(cid:173)
`p