Advanced Drug Delivery Reviews 23 H997) 3-25
`
`cidvonced
`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
`('wtIrnI Ru.wim‘I1 I)ii'itum. I-ffi:m' IIl('.. Gmtun. CT 06140. USA
`
`Received 9 August I996; ttcceptcd I4 August I990
`
`Abstract
`
`Experimental and computational approaches to estimate solubility and permeability in discovery and development settings
`are described. in 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 H-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. Turbidimctric 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‘ ln the development setting. solubility calculations focus on exact value prediction and are diftieult 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
`measurenients.
`
`Ke_\'u‘ords: Rule of 5: Computational alert: Poor absorption or permeation; MWT; Mbogl’; H-Bond donors and acceptors;
`Turbidimeiric solubility: Thermodynamic solubility: Solubility calculation
`
`(‘ontents
`
`Introduction
`4
`............................................. ,.
`. Theilnigiliscovery setting...... .
`7. I. (‘hanges in drug leads and physicn-chemnral properties . . t
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`Factors affecting phycicmchemical lead profiles .
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`Identifying '.| libniry with favorable physiemchemical properties
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`The target audience — medicinal chemists ............................................................................. ..
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`Culculuted properties of the ‘US/\N‘ libniry . . .
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`The ‘rule of S‘ and its implementation ...................................................................................................................... ..
`Urally active drugs outside the ‘rule of 5‘ mnemonic and biologic transporters .............................................................. ..
`High MWT USANS and the trend Ill lflbtvgl’ .............................................. ..
`. New chemical entities. calculatioiis.
`. Drugs in absorption and permeability studies. calculations ..
`. Validating the computational alert .................................................................................................................................. ..
`. Chztnges in calculated physical property profiles at Pfizer ................................................................................................ _.
`. The rationale for measuring drug solubility in it discovery setting ....................... ..
`Drugs have high turbidimetnc solnbilit_v ..................................................................................................................... ..
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`55-J
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`Toncsmnding author. Tel:
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`-l-I 860 4-ll356|: e—mail: L[PlNSKl@PFlZER.COM.
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`OIGV-~l09X/97/$32.00 Copyright
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`‘-1)
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`I997 Elsevirr Science B.\/. All rights reserved
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`PII Slllh9—409Xl96)O0423-I
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`2.l5. High throughput screening hits. calculations and solubility measurements ........................................................................ ..
`2.l6. The triad of potency. solubility and permeability ............................................................................................................ ..
`2.|7. Protocols for measuring drug solubility in a discovery setting.
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`2.18. Technical considerations and signal processing . . . . . . . . .
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`3. Calculation of absorption parameters ................. ..
`3.]. Overall approach . . . .
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`32. MLogP. Log P by the method of Moriguchi ..
`3.3. MLogP calculations ...................................................... ..
`4. The development setting: prediction of aqueous thermodynamic solubility ................................................................................ ..
`4.|. General considerations ................................................................................................................................................... ..
`4.2. LSERs and TLSER methods ........................................................................................................................................... ..
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`4.3. LogP and AQUAFAC methods ....................................................................................................................................... ..
`4.4. Other calculation methods .............................................................................................................................................. ..
`5. Conclusion ............................. ..
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`References ............................................................................................................................................................................... ..
`
`
`1. Introduction
`
`
`
`
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`
`
`2. The drug discovery setting
`
`This review presents distinctly different but com-
`plementary experimental
`and computational
`ap-
`proaches to estimate solubility and permeability in
`drug discovery and drug development settings, In the
`discovery setting, we describe an experimental ap-
`proach to turbidimetric solubility measurement as
`well as computational approaches to absorption and
`permeability. The absence of discovery experimental
`approaches to permeation measurements refiects the
`authors’ experience at Pfizer Central Research. Ac-
`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-
`like compounds. This review deals only with solu-
`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
`chemistry physical property profiles may be the
`result of leads generated through high throughput
`screening. In the development setting. computational
`approaches to estimate solubility are critically re-
`viewed based on current computational
`solubility
`research and experimental solubility measurements.
`
`Page 2 of 23
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`2.]. Changes in drug leads and ph_,vsic0—c/iemical
`
`properties
`
`the sources of drug leads in the
`In recent years,
`pharmaceutical
`industry have changed significantly.
`From about 1970 on, what were considered at that
`
`time to be large empirically—based screening pro-
`grams became less and less important
`in the drug
`industry as the knowledge base grew for rational
`drug design [I]. 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-
`ings; published reports
`in scientific journals and
`collaborations with academic investigators. Most of
`these lead sources had the common theme that the
`
`lead’ already had undergone considerable
`‘chemical
`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.
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`C.A. Lipimki er a1.
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`/ Advanced Drug Delivery Re\'iew.\' 23 (1997) 3-25
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`U1
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`often compensated for by the follow-up to the
`primary screen. This is typically a more careful,
`more labor-intensive process of in vitro retesting to
`determine lC50s 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-
`
`dynamic aqueous solubility is not very relevant to
`the screening manner in which leads are detected.
`
`This situation changed dramatically about 1989-
`1991. Prior to 1989, it was technically unfeasible to
`screen for
`in vitro activity across hundreds of
`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-
`ible to screen hundreds of thousands of compounds
`across in vitro assays [2—4]. Combinatorial chemis-
`try soon began' 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-
`ceptor subtypes in cells lacking receptors that might
`interfere with an assay and by the construction of
`receptor constructs to facilitate signal detection. The
`screening of very large numbers of compounds
`necessitated a radical departure from the traditional
`method of drug solubilization. Compounds were no
`longer solubilized in aqueous media under thermo-
`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 drugs could be tested because the kinetics
`of compound crystallization determined the apparent
`‘solubility’ level. Moreover, compounds could parti-
`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 p.M 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-
`bility achieved by equilibration of a well character-
`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 Scisearch and Chemical Abstracts for refer-
`ences to combinatorial chemistry in titles or descriptors using the
`truncated terms COMBIN? and Cl-IEMISTR? gave the following
`number of references respectively: 1990, 0 and 0; 1991, 2 and 1;
`1993. 8 and 8; 1994, 12 and 11'. 1995. 46 and 45.
`
`
`
`2.2. Factors affecting ph_,vsic0-chemical lead
`profiles
`
`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-
`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-
`ship (SAR) studies.
`One of the most reliable methods in medicinal
`
`chemistry to improve in vitro activity is to incorpo-
`rate properly positioned lipophilic groups. For exam-
`ple, addition of a single methyl group that can
`occupy a receptor
`‘pocket’
`improves binding by
`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-
`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
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`6
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`(TA. LipirI.\'/ti 1'! al.
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`/ Al/l’£lIl('(’d Drug De/iiwjx‘ Rl’\’l(’ll'A' 2] (I997) .?~.’5
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`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-
`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 p.M 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-
`philic, larger. less soluble compounds. Tight control
`of the criteria for activity detection in the initial HTS
`screen minimizes labor—intensive secondary evalua-
`tion and minimizes the effect of human biases. The
`
`
`
`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-
`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-
`able range of measured or calculated physico—chemi-
`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-
`up is decreased but
`less potent, perhaps physico-
`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 toxic
`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-
`tion of lead attractiveness is
`to examine the re-
`
`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 C/ieniisrry. No single pharmaceutical com-
`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.
`
`Iclentifving [1 library with favorable ph_ysi(‘0—
`2.3.
`t‘/1emi(‘aI pmpertie.s'
`
`The idea in selecting a library with good absorp-
`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-
`chemical properties would predominate in the many
`
`detectable in HTS screens if they are larger and more
`lipophilic.
`The nature of the screen determines the physico-
`chemical profile of the resultant ‘hits’. The larger the
`number of hits
`that are detected.
`the more the
`
`lower potency hits with more
`that
`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-
`mediate or
`the expression level of M-RNA or
`protein. Multiple mechanisms may lead to the mea-
`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
`being screened. Perhaps.
`equally importantly.
`a
`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-
`chemical profile of an HTS ‘hit’. Obviously high
`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-
`
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`

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`CA. Lipinski et ul.
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`/ Advam‘t’d Drug Delivery Reviews 23 (1997) 3-25
`
`7
`
`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.
`
`
`
`to survive pre«
`compounds that enter into and fail
`clinical stages and Phase I safety evaluation. We
`expected that the most insoluble and poorly perme-
`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-
`prietary Name (INN) names to identify compounds
`entering Phase II since most drug companies (includ-
`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 H76
`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-
`taining the fragment O = P-O. 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-
`
`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
`
`intestinal and blood brain barrier
`relating poorer
`permeability to increasing molecular weight
`[7,8]
`and the more rapid decline in permeation time as a
`function of molecular weight
`in lipid bi—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 1 1% 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.
`
`ratio of octanol
`a
`Lipophilicity expressed as
`solubility to aqueous solubility appears in some form
`in almost every analysis of physico—chemical prop-
`erties related to absorption [10]. The computational
`problem is that an operationally useful computational
`alert
`to possible absorption—permeability problems
`must have a no fail
`log P calculation.
`ln 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
`inordinate number of
`‘strange fragments’
`in our
`chemistry libraries but rather lies in the direction of
`the trade off between accuracy and ability to calcu-
`late all compounds adopted by the Pomona College
`
`Page 5 of 23
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`8
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`CA. Li{}iI?.S'ok'f
`
`(*3 (if. / Azfizzmm’ {mg 2‘)z*/‘[t‘ery R(’l'f</H’.s'
`
`.73 ( 2997) 3-25
`
`the
`to allow for
`steric modifier
`with perhaps a
`interactions between donor and acceptor moieties.
`Experimental 0L values for hydrogen bond donors and
`[3 values for acceptor groups [17] have been com-
`piled by Professor Abraham in the UK and by the
`Raevsky group in Russia ]l8,l9]. Both research
`groups currently express the hydrogen bond donor
`and acceptor properties of a moiety on a thermo-
`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. lmportantly, this pa-
`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-
`
`ture links with better permeability.
`Too many hydrogen bond acceptor groups also
`hinder permeability across a membrane bi—1ayer. The
`sum of Ns and Os is a rough measure of H bond
`accepting ability. This very simple calculation is not
`nearly
`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
`O 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 (1.33 on
`the C scale). The more accurate solvatochromic B
`parameter which measures acceptor ability varies far
`more on a per nitrogen or oxygen atom basis than the
`corresponding 01 parameter. When we examined the
`USAN library we found a fairly sharp cutoff in
`profiles with only about 12% of compounds having
`more than 10 Ns and Os.
`
`team. The CLogP calculation emphasizes high ac-
`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. [11] within the Molec-
`ular Design Limited MACCS and ISIS base pro-
`grams to avoid the missing fragment problem. As 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 [ 12.13].
`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 (_\~
`axis) is less than unity. At the high log P end,
`the
`Moriguchi MLogP is
`somewhat
`lower
`than the
`MedChem CLogP. 1n 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-
`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-
`layer ]14,l5]. Hydrogen donor ability can be mea-
`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 ]l5] or as the log of the
`ratio of octanol to hydrocarbon partitioning. In vitro
`systems for studying intestinal drug absorption have
`been recently reviewed ]l6]. Computationally, hy-
`drogen donor ability differences can be expressed by
`the solvatochromic or parameter of a donor group
`
`
`
`2.6. The 'rule of 5‘ and its implementation
`
`At
`
`this point we had four parameters that we
`
`Page 6 of 23
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`Page 6 of 23
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`C.A. Lipinski et al.
`
`/ Advanced Drug Delivery Re\'iew.r 23 (1997) 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
`confidence level of an assay at 90 or 95% we asked
`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
`mnemonic which we called the ‘rule of 5’
`[20]
`
`because the cutoffs for each of the four parameters
`were all close to 5 or a multiple of 5. In the USAN
`set we