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Perspective

Contribution of Molecular Properties to Drug Promiscuity Ákos Tarcsay, and Gyorgy M Keseru J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/jm301514n • Publication Date (Web): 15 Jan 2013 Downloaded from http://pubs.acs.org on January 16, 2013

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Journal of Medicinal Chemistry

Contributions of Molecular Properties to Drug Promiscuity Ákos Tarcsay1 and György M. Keserő2,* 1, Discovery Chemistry, Gedeon Richter plc. 19-21 Gyömrıi út, H-1103 Budapest, Hungary, 2, Research Centre for Natural Sciences, Hungarian Academy of Sciences, 59-67 Pusztaszeri út, H-1025 Budapest, Hungary

TITLE RUNNING HEAD Molecular obesity makes compounds promiscuous

Abstract In contrast to designed polipharmacology that can result in efficient drugs for complex disorders, unintended drug promiscuity has detrimental contribution to side-effects and toxicology. Characterization of promiscuous compounds enhances the understanding of complex interaction patterns and aids the design of compounds with broader selectivity against off-targets that has a major impact on medicinal chemistry outcome. In this Mini-Perspective we provide insights to the effect of physicochemical parameters on promiscuity. Information collected from recent, large-scale in vitro studies enabled us to discuss the relationships between physicochemical properties and promiscuity in details. In light of these data, lipophilicity and basic character have the highest influence. Based on the accumulated knowledge, we propose the extensive use of pre and post-synthesis metrics, as well as strict control of physicochemical properties during medicinal chemistry optimizations.

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Introduction 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Physicochemical properties have a major impact on the efficacy and safety of drug candidates. More importantly, these are the parameters directly controlled by medicinal chemists in multidimensional optimizations. Case studies and retrospective analyses of physicochemical property changes in lead discovery and optimization therefore serve as a valuable pool of medicinal chemistry experience. Analysis of time-dependent physicochemical property changes of marketed drugs revealed that unlike molecular weight (MW)1,2, that significantly increased, lipophilicity (estimated by logP) has less variability in time.2 By contrast, the lipophilicity of preclinical compounds has been increased significantly at least in the last 20 years.3 This undesired shift in physicochemical properties – often described as property inflation – has first been documented in lead optimization programs2 but later it was traced back to the lead discovery phase.4 More recently uncontrolled potency optimization has been identified as a major driver of potency inflation and the resulting syndrome described as molecular obesity5. Positive correlation between MW, logP and potency for more than 200 000 ChEMBL compounds6 supports this conclusion. In fact, these authors showed that 41% of the nanomolar potency ChEMBL compounds had MW>400 and AlogP>4. In contrast only 8% of the oral drugs had similar parameters.6 Thorough analyses of binding thermodynamics revealed that increasing the lipophilicity i.e. making compounds “fatter” is a relatively simply way of potency optimization.7, 8, 9 This kind of potency addiction results in high-affinity compounds, but they are highly lipophilic lead series and candidates5 and thus have higher chance of attrition in drug discovery and development. In contrast, investigating lead series that were successfully optimized to drugs Perola, concluded that lipophilicity is preserved in most of the successful optimization programs.10 The impact of controlling physicochemical parameters, especially lipophilicity, has also been shown on dual serotonin–norepinephrine reuptake inhibitor (SNRI) pharmacology and off-target promiscuity11 and on the successful quest for oral factor Xa inhibitors.12 The impact of physicochemical parameters on promiscuity


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Lipophilicity has a major impact on a number of druglike features including ADME parameters 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

and toxicological properties.13 Lipinski and colleagues demonstrated the relationship between poor absorption, solubility and permeability and increased values of MW, logP, and H-bonding partners for a dataset of 2245 compounds in clinical development.14 Detrimental effect of increasing lipophilicity was evidently shown on a large GSK dataset (~30 000 diverse compounds) with 15 measured in vitro ADMET parameters that are integral to preclinical pharmacokinetic screens.15 Similar deteriorating effect of increasing lipophilicity was shown on an in vitro safety endpoint of 119 central nervous system (CNS) drugs and 108 candidates16 and also for 273 commercially available drugs (either marketed or withdrawn from the market) and 273 Pfizer proprietary new chemical entities.17 Animal in vivo tolerance studies involving 245 Pfizer preclinical compounds highlighted the impact of lipophilicity and topological surface area (TPSA) on toxicological outcomes, that led to the formation of the “clogp75”-rule to avoid in vivo risk factors.18 When both factors are present a very clear and consistent trend was observed. Compounds with high-clogP/low-TPSA are approximately 6 times more likely to be toxic compared to the low-clogP/high-TPSA compounds. More recently, a GSK team showed that increasing lipophilicity contributes to lower drug efficiency and consequently higher required doses that may increase the risk of adverse effects.19 Compound promiscuity is a Janus-faced property, since it might be needed for in vivo efficacy and obviously the goal of network or poly pharmacology approaches.1,20 Promiscuity is exploited by the human body during detoxification and protection from xenobiotics in biological barriers. Transporters, like P-glycoprotein (Pgp), and enzymes involved in drug metabolism, such as CYP3A4, are evolved to interact with the wide range of unrelated chemical structures of xenobiotics.21,22 20 On the other hand, high promiscuity and multiple interactions with off-targets have significant detrimental effect23 on safety properties. The essential role of promiscuity on the fate of compounds has been demonstrated very recently.24. The promiscuity analysis of clinical and development-stage candidates from Novartis highlighted that discontinued clinical candidates and withdrawn drugs can be characterized by elevated promiscuity.24 Although



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promiscuity can be traced back to several compound quality related sources including non1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

specific mode of action25, contaminants such as heavy metals, polymers and synthetic impurities26, the outstanding impact of physicochemical properties has been evidently demonstrated on numerous large-scale studies (Table 1.).

Table 1. Promiscuity-property relationships reported by different organizations

Number of

Number of

compounds’

assays

Physicochemical

Dependency on

properties

physicochemic

investigated

al properties

Organization

75000 inPfizer

Mw, logP

Reference

MW: negative

220 in-house

27

house

logP: positive

1098 drugs

logP 70 Cerep

and 430 Pfizer

BioPrint

logP: positive

27

project panel compounds 48 Cerep

logP, TPSA

108 clinical Pfizer

logP: positive BioPrint

18

candidates

TPSA: negative panel Promiscuity

Organon

Mw, logP

is defined as

MW: negative

low

logP: positive

138 in-house

28

selectivity Mw, AlogP, HBA, 50-79 inNovartis

3138 in house

MW: positive HBD, #o, #N, RotB,

house

23 logP: positive

#rings, ring


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assemblies, terminal 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

rotamers, chain assemblies Mw, clogP, pKa,

MW: positive

#rings

logP: positive

2133 drugs AstraZeneca

and reference

200 Cerep

2 pKa: positive

compounds for bases In silico: Mw, Nhev, HBD, HBA, RotB, #rings, #Ar rings, # Non-Ar rings, PSA, MW: no clogP, logP: positive Roche

213 in-house

80 Cerep

amphiphilicity, pKa,

29 pKa: positive

ionization state, for bases clogD Measured: solubility, logD(7.4), permeability, pKa

AstraZeneca

2267 drugs

200 Cerep

Nhev, clogP and

Nhev: bell

and reference

BioPrint

molecular framework

shaped

compounds

panel

(fMF)

logP: positive

MW, logP, pKa

MW: positive

30

logP: positive 40 408

>500

ChEMBL

pKa: positive literature

literature for neutrals and bases


6


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Chrom logP and #Ar

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>2500 drugs

>490 in

logP+#Ar:

and in-house

house

2413 drugs

141 safety-

Mw, clogp, pKa,

logP: positive

and druglike

relevant

HBA, HBD, #Ar,

pKa: positive

compounds

targets

PSA, RotB

for bases

logP, pKa

logP: positive

31

GSK

Roche

positive

32

pKa: positive Novartis

656 drugs

73 in-house

33 for neutrals and bases

Among the several physicochemical parameters that were investigated on different large-scale datasets, lipophilicity, MW and basic character were found to have the most significant correlation with promiscuity. It should be noted, however, that promiscuity index depends heavily on the composition of the panel and the number of targets included.24 Hopkins and coworkers analyzed 75 000 compounds selected from high-throughput screening (HTS) campaigns in Pfizer involving 220 assays with measured IC50 value.27 They observed negative correlation between the mean Mw of compounds and the average number of active targets in each bin (IC50< 10 µM). Although this observation seems to be in line with the complexity theory,34 most of the other studies found this relationship to be more complex. In the same Pfizer study, BioPrint full matrix data set with approximately 1500 compounds indicated that promiscuity correlates with lipophilicity, and revealed that compounds possessing clogP>2.5-3 tend to be active on more than 10 assays.27 Investigating 138 preclinical compounds from the SCOPE database Morphy and Rankovic of Organon found similar trends i.e. decreasing size and increasing logP enhanced the chance of promiscuity.28 It should be noted that these authors used a non-standard definition of promiscuity (defined as the sum of the two selectivity values in log units for the targets) thus considering the less selective compounds as more promiscuous in their rather small dataset. The proposed negative correlation between Mw and


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promiscuity was not validated by other studies. Azzaoui and coworkers from Novartis analyzed 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

3138 compounds that have been tested in at least 50 out of 79 assays.23 The calculated AlogP and Mw were significantly higher for promiscuous compounds than for the selective compounds. Hughes and colleagues from Pfizer tested their 3/75-rule on 108 compounds submitted to 48 assays of CEREP BioPrint panel.18 Compounds demonstrated >50% activities at 10 µM in three or more assays out of 48 were marked as promiscuous. This measure of promiscuity strongly associated to higher clogP and lower TPSA similarly to the in vivo toxicity data: if both risk factors were present, 25-fold higher chance for promiscuity was observed. Peters and coworkers from Hoffmann-La Roche collected 213 recent in-house compounds that were profiled at Cerep within 2004-2007 and found no correlation between promiscuity and molecular descriptors other than pKa and clogP.29 Leeson and Springthorpe scrutinized the Cerep BioPrint database of drugs and reference compounds reporting positive correlation between clogP and promiscuity.2 These authors found the relationship between promiscuity and Mw being complex. In contrast to the findings of Hopkins et al., bases and neutral compounds showed optimal promiscuity in the 350500 Da range, while promiscuity correlated positively with the Mw of acids. Similar findings on the same BioPrint database was published by Yang and coworkers from AstraZenaca: logP correlated positively with promiscuity but plotting heavy atom count and promiscuity showed bell-shaped curve for 2267 compounds.30 Gleeson and coworkers assessed the promiscuity using the ChEMBL database and found positive correlation between promiscuity and lipophilicity, and between promiscuity and Mw for ~ 40 000 compounds.6 Acidic molecules were found to be less promiscuous than neutral or basic ones and for lower Mw ranges bases had higher promiscuity compared to neutral compounds. Young and colleagues from GSK inspected 2500 compounds including marketed drugs, compounds from GSK lead optimization projects, legacy leads and failed development candidates that were screened in > 490 assay.31 The Property Forecast Index (PFI, Chrom-logD plus aromatic ring count) suggested by these authors showed strong positive correlation with promiscuity. Peters and coworkers from Roche and Cerep found that



promiscuity of 2413 drugs and drug-like compounds screened against 141 safety-relevant targets 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

correlates with clogP and the correlation is more pronounced for basic compounds.32 In a recent paper Lounkine and coworkers developed and assessed a large-scale similarity ensemble approach (SEA) method for prospective evaluation of safety target prediction. In toal, 656 drugs were computationally screened for their likelihood to bind 73 Novartis in vitro safety targets coupled to adverse drug effects.33 Out of the 47000 possible drug-target pairs 1644 had significant SEA expectation value. Of out these pairs, 403 were already known in ChEMBL and were therefore considered as confirmed and 348 predictions were found in the proprietary ligand-target databases unavailable to SEA. Of the remaining 893 predictions 694 were tested at Novartis and in 151 cases IC50 values of less than 30 µM were measured. Focusing on the novel 1042 predictions, 348 were confirmed by secondary database search and 151 by assays that results in 48% confirmed prediction rate. These results are encouraging especially since the protocol allows the direct prediction of drug-target pairs having importance in designing in vitro safety screens. Unfortunately, however, we have virtually no information on negative predictions and the rate of successful predictions calls for further improvements. In accordance with publicly available data collected on tens of thousand of compounds investigated in hundreds of biological assays the authors found that higher lipophilicity and the basic character are interrelated to higher promiscuity. Inspired by this well-established relationship we were interested in assessing the prediction power of lipophilicity (AlogP) on the drug-target pairs used in this study. Compounds measured in at least 40 assays were binned by their AlogP and the average hit rate was plotted for all compounds, and for subsets with negative, neutral or positive formal charge at pH 7.4 (Figure 1.) The decreased hit rate observed at high AlogP might be due to the limited solubility of the compounds.


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140

0,6

120

0,5

100

Hit rate

0,4

80

0,3

60

0,2

40

0,1

Number of cases

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


20

0,0

0

-0,1 -8

-6

-4

-2

0

2

4

6

8

Binned AlogP

Figure 1. Relationship between AlogP and hit rate on off-targets for 638 marketed drugs investigated on 40-73 targets.33 Left Y-axis shows the hit rate, black, green, orange and red lines and symbols represents all, neutral, acidic, and basic compounds, respectively. Right Y-axis and blue line with symbols represents all compounds regardless the ionization state (corresponds to black squares of left axis).

Significant correlation between AlogP and the hit rate on side-effect targets was found, that is more pronounced in the case of positively charged compounds and less evident in the case of negatively charged compounds compared to neutral ones. Next we analyzed the cumulative AlogP distribution in the whole database, for compounds with confirmed predictions and for compounds with novel off-targets found in the recent Novartis study (Figure 2.). Significant differences were found between the whole database and the compounds confirmed prediction, and between the whole database and the compounds with identified novel off-targets (Kolmogorov-Smirnoff test p< 0.001). These results indicate that higher lipophilicity has a significant correlation with safety-relevant targets and the found novel off-target pairs.



Total Adverse (off-target)

Percent of compounds

100

80

60

40

20

0 -4

-2

0

2

4

6

AlogP

Figure 2. Cumulative distributions of all cases (black square), novel off-target compound collection (red rhombus) and confirmed predictions (green triangles). Dashed blue and orange lines represents 80% and AlogP=3 values, respectively.

Finally, we investigated AlogP as being able to discriminate promiscuous compounds that hit more than five off-targets. (Figure 3).

100 90 80 70 60

%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

50 40 30 20 10 0 2

3

4

5

6

AlogP cut-off

Figure 3. Impact of AlogP on the number of hit targets. Green colored bars represents the percent of non-promiscuous compounds (hit targets ≤5), blue represents the promiscuous (hit targets >5)


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compounds, dark color represents the percent of the cases with AlogPAlogPcut-off.

In line with previous studies the Lounkine database again suggests that lipophilicity itself is indicative for adverse promiscuity. Investigating different values for the AlogPcut-off range of 2 and 6 we found that 67% of the compounds with AlogP larger than 4 can be considered promiscuous hitting more than 5 targets at 30 µM (see the light blue versus light green parts of the column at AlogPcut-off = 4 on Figure 3). On the other hand, the 65% of the less lipophilic compounds (AlogP < 4) hit ≤5 targets (see the dark green versus dark blue parts of the column at AlogPcut-off = 4 on Figure 3). These observations suggest that AlogP = 4 might be a suitable threshold separating promiscuous compounds from non-promiscuous ones. In fact, reevaluation of the Lounkine dataset revealed that the off-target hit rate was significantly (two-sample t-test p4 (mean hit rate: 0.23) than that calculated for compounds having AlogP < 4 (mean hit rate: 0.10). Interestingly the median AlogP for the whole set was found to be 2.4 and 79% of the compounds had lower value than 4. This is in good agreement with the findings of Paolini et al. and Gleeson et al. reporting that the median MW and clogP of 617 approved drugs is 316 and 2.3, respectively1 and only 8% out of the 1791 oral drugs had both a Mw >400 and AlogP>4.6 Therefore, the incorporation of physicochemical descriptors, particularly lipophilicity, into complex, similarity based in silico models of adverse interactions, is entirely justified. Since the Novartis dataset comprises approved drugs it implies that there are numerous factors beyond, such as the therapeutic dose, the human pharmacokinetics and the critical assessment of side-effects versus benefits, that are especially situation dependent. Experimental data suggest that logP and basic pKa are the two most important physicochemical parameters that impact compound promiscuity. Lipophilicity correlates with desolvation energy therefore as the lipophilicity is higher, compounds tend to be dehydrated from the bulk water15 and bound in the generally more lipophilic protein environment Hydrophobic interactions are



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typically less oriented and less dependent on geometric factors compared to polar interactions 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(H-bonds, salt bridges and other polar-polar interactions). Lipophilic compounds could therefore bind to a broad spectrum of binding sites simply by the virtue of hydrophobic interactions driven by the gain in desolvation entropy. This effect could even compensate limitations in electrostatic complementarity and some of the enthalpic penalties. On the other hand, examining the reasons behind the effects of basic character and promiscuity we note here that most of the promiscuous targets are aminergic GPCRs and ion channels permeable for cations.1,

24, 33

The suggested

minimal panel of side-effect targets recently suggested by Bowes and co-workers24 incorporates 44 targets including 24 GPCRs and 7 ion channels. Binding site of aminergic GPCRs such as histamine, dopamine, muscarinic acetylcholine or serotonin receptors incorporate a key acidic residue that is responsible for ligand coordination, therefore preferring basic molecules. The composition of cation channels also evolved to coordinate ligands bearing positive charge explaining the observed increased promiscuity of basic compounds. The promiscuity of basic compounds can be traced back to the nature of these binding sites. Perspectives to control promiscuity Based on the large body of evidence published in the last few years promiscuity and the risk of side-effects are clearly elevated for more lipophilic compounds. The detrimental effect of high lipophilicity on promiscuity related to adverse drug reactions and ADMET properties argues for the strict control of lipophilicity in medicinal chemistry programs. Considering the future need to deal with unwanted promiscuity we want to highlight three areas. First, the hit identification has crucial impact on compound quality in terms of physicochemical parameters. In order to obtain desirable physicochemical parameters and enthalpy driven binding it is recommended to focus on small molecules e.g. from fragment screening.35 Second, during the course of medicinal chemistry optimization the pre-synthesis phase decisions in design teams should be based on predicted physicochemical properties, particularly on lipophilicity measurements or estimates. Empirical rules such as the Pfizer 3/7518 or the GSK 4/400 rule15 and more complex metrics such as the MPO score,16 DrugScore,6 Property forecast index (PFI)31 or the sweet spot approach36


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would give further guidance for selecting the most promising candidates for synthesis. Third, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

post-synthesis evaluation of compounds should be based on lipophilic efficiency metrics such as LLE2 or LELP,4,37 that favor high quality leads and drugs. Relationships found between LELP values and thermodynamic signatures revealed that compounds possessing better LELP values tend to bind in a more enthalpic manner.37 These highly oriented, specific ligand-protein interactions significantly contribute to selectivity38 and therefore reduce the risk of undesirable promiscuity. The observed relationship between thermodynamics and selectivity is in line with the increased promiscuity of more lipophilic compounds. Since increasing lipophilicity generally increases the binding affinity by means of entropic contributions, and in contrast, the introduction of polar groups in order to increase the binding energy by enthalpic interactions decreases the lipophilicity. Metrics such as the Compound Safety Evaluator (CSE) score39 might help prioritize compounds for early safety studies. The promising SEA approach developed at Novartis is certainly important for suggesting how to improve the prediction of potential side-effect targets. However, it should be kept in mind that lipophilicity is one of the key properties that characterize the fate of chemical entities in drug development. Lipophilicity shows clear correlation with ADMET features, promiscuity, and offtarget interactions as documented in a high number of studies2, 6,

15, 18, 16, 26-33

based on large,

publicly available or proprietary databases. In certain therapeutic areas modulation of targetnetworks by multitarget or polypharmacology approaches seems to be beneficial for clinical success. The promiscuity discussed here, however, is much less attractive i.e. candidates should have exclusively designed target profile to avoid unwanted target interactions invoking toxicity.40,41 The collected knowledge suggests rigorous use of the predicted physiochemical parameters and implicates a design workflow similar to that depicted on Figure 4.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 4. Proposed workflow for medicinal chemistry programs. The size of the arrows represents the number of compounds in each step.

Independently from design principles used, (e.g. computational methods, SAR analysis or analogue-based medicinal chemistry approaches) we argue that the proposed chemical space should be first enumerated and subsequently filtered by predicted physicochemical parameters. There are multiple factors behind this suggestion. First, the number of compounds fitting to the applied design principle is usually higher than that could be manually considered. The systematic enumeration of compounds within the design space could therefore ensure that each and every option is considered. More importantly, this type of enumeration is basically free from human bias such as previous experience, synthetic feasibility and other factors within the knowledge domain of individual medicinal chemists. Second, chemists have much deeper knowledge on structure-physicochemical property relationships and they are typically using this experience to influence biological properties. Simple physicochemical properties are easy to calculate and they are among the best predicting properties. Third, although chemists have strong intuitive


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knowledge on physicochemical properties, systematic evaluation by computational tools 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

provides fast, reliable and uniform treatment for all of the compounds within the design space. And last, the number of compounds fulfilling the design criteria and physicochemical filters are still too large for direct synthesis that allows room enough for more sophisticated medicinal chemistry considerations and more importantly for the crucial contribution of human intuition and invention. Conculsions Drug discovery is such a cost intensive and risky business with extremely long term goals that it requires simply the best from all contributing disciplines. Due to its central position in the heart of small molecule drug discovery, medicinal chemistry simply can not afford to be responsible for increasing the inherent risk by compounds with inferior quality. Translating the dramatic costs of discovery and development into enhanced productivity necessarily demands zerotolerance on chemistry-related toxicology issues. Suboptimal compounds are therefore no longer accepted as the output of shrinking small molecule drug discovery pipelines. Although developing compounds with optimal balance between potency and physicochemical properties represents a much greater challenge, it seems to be the only viable medicinal chemistry strategy to increase pharmaceutical productivity.

Acknowledgement The authors are grateful to Mike Hann (GSK) for helpful discussions. Author information Phone: +36-20-319-0986. E-mail: [email protected]

Biographies Ákos Tarcsay received M.Sc. from biochemical engineering in 2008 and chemical engineering in 2009 at the Budapest University of Technology and Economics, Hungary. He has been



working in the CADD group of the Discovery Chemistry Laboratory, Gedeon Richter Plc. as a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

research scientist since 2009. The main topics of his Ph.D. studies are the development and application of computational tools to aid ADME optimization under the supervision of György M. Keserő.

György M. Keserő obtained his Ph.D. at Budapest, Hungary and joined Sanofi-Aventis CHINOIN heading a chemistry research lab. He moved to Gedeon Richter in 1999 as the Head of Computer-aided Drug Discovery and from 2007 he served as the Head of Discovery Chemistry. He contributed to the discovery of the antipsychotic Cariprazine® that is under NDA filing. Since 2013 he was appointed as the general director of the Research Centre for Natural Sciences at the Hungarian Academy of Sciences His research interests include medicinal chemistry and drug design. He has published over 150 papers and more than 10 books and book chapters.

Abbreviations ADME, absorption, distribution, metabolism, excretion; CNS, central nervous system; Mw, molecular weight; Pgp, P-glycoprotein; SNRI, serotonin–norepinephrine reuptake inhibitor; TPSA topological surface area

References (1) Paolini G.V.; Shapland, R.H.; van Hoorn, W.P.; Mason, J.S.; Hopkins, A.L. Global mapping of pharmacological space. Nat Biotechnol. 2006 24 (7), 805-815. (2) Leeson, P.D.; Springthorpe, B. The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov. 2007, 6 (11), 881-890. (3) Walters, W.P; Green, J.; Weiss, J.R.; Murcko, M.A. What do medicinal chemists actually make? A 50-year retrospective. J Med Chem. 2011, 54 (19), 6405-6416.


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(4) Keserő, G.M.; Makara, G.M. The influence of lead discovery strategies on the properties of 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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2011.

Slides

are

available

at

http://www.soci.org/News/~/media/Files/Conference%20Downloads/Designing%20Safer%20M edicines%20in%20Discvery%20Mar%202011/Kevin_Dack_Presentation.ashx (40) Leeson, P.D., Empfield, J.E.; Reducing the Risk of Drug Attrition Associated with Physicochemical Properties , Annual Reports in Medicinal Chemistry 2011, 45 (24) 393-407 (41) Hopkins, A.L.; Drug discovery: Predicting promiscuity, Nature. 2009, 462 (7270), 167-168.


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