Lead-like Drugs: A Perspective - Journal of Medicinal Chemistry (ACS

Jul 27, 2018 - Lead-like drugs, or drugs below molecular weight 300, are an important and sometimes overlooked component of the current pharmacopeia ...
0 downloads 0 Views 1MB Size
Perspective Cite This: J. Med. Chem. XXXX, XXX, XXX−XXX

pubs.acs.org/jmc

Lead-like Drugs: A Perspective Miniperspective Brian Raymer* and Samit K. Bhattacharya* Medicine Design, Pfizer Worldwide Research and Development, 1 Portland Street, Cambridge, Massachusetts 02139, United States

Downloaded via UNIV OF SUSSEX on July 28, 2018 at 14:02:06 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

S Supporting Information *

ABSTRACT: Lead-like drugs, or drugs below molecular weight 300, are an important and sometimes overlooked component of the current pharmacopeia and contemporary medicinal chemistry practice. To examine the recent state-of-the-art in lead-like drug discovery, we surveyed recent drug approvals from 2011 to 2017 and top 200 prescribed medications, as well as provide case studies on recently approved lead-like drugs. Many of these recent drugs are close analogs of previously known drugs or natural substrates, with a key focus of their medicinal chemistry optimization being the choice of a low molecular weight starting point and maintaining low molecular weight during the optimization. However, the identification of low molecular weight starting points may be limited by the availability of suitable low molecular weight screening sets. To increase the discovery rate of leadlike drugs, we suggest an increased focus on inclusion and prosecution of lead-like starting points in screening libraries.

In small proportions we just beauties see, And in short measures life may perfect be.



Ben Jonson. From Underwoods (1640): “To the Immortal Memory of That Noble Pair, Sir Lucius Cary and Sir Henry Morison”

INTRODUCTION Lead-like drugs, or drugs below molecular weight (MW) 300, are an important and sometimes overlooked component of the current pharmacopeia and contemporary medicinal chemistry practice. 1,2 In 2011, Walters and colleagues collated physicochemical properties of approved drugs across a 50year span. They found typical molecular weight ranges of approved drugs as 300−360 during the earliest time period (1959−1964), with a trend upward over time to 360−440 for the most recent time period (2005−2009). Taken together, it is reasonable to conclude that on average, the molecular weight of drugs typically falls within the range of 300−440. The molecular weight boundary of 300 for lead-like drugs also conveniently coincides with the molecular weight criteria of the rule-of-three (Ro3), although that value is focused on fragment selection for fragment-based drug discovery.3 Given these ranges and trends in molecular weight, we propose that drugs below a molecular weight of 300 can be considered atypical and use this criteria as an upper boundary for lead-like drugs.4 To examine the recent state-of-the-art in lead-like drug discovery, we surveyed drug approvals [by Food and Drug Administration (FDA) and European Medicines Agency (EMA)] between 2011 and 2017 (Figure 1; see Supporting © XXXX American Chemical Society

Figure 1. Number of new small molecule drug approvals and highly prescribed drugs, binned by molecular weight ranges: top 200 U.S. prescribed drugs in 2014 (red bars); drugs approved from 2011 to 2016 (blue bars); drugs approved in 2017 (green).

Information for the full list of drugs), excluding drugs above MW 2000. We also analyzed a list of the top 200 prescribed medications (branded and generic) from 2014 U.S. prescription data.5 The figure highlights the number of lead-like drugs recently approved and, moreover, the long-term success of lead-like drugs based on prescription levels of the top 200 prescribed medications. During 2011−2016, 25 of 146 recent drug approvals (17%) were below the typical molecular weight range of 300−440. As to the top 200 prescribed medications, 57 of the 160 low molecular weight drugs had a molecular Received: March 13, 2018

A

DOI: 10.1021/acs.jmedchem.8b00407 J. Med. Chem. XXXX, XXX, XXX−XXX

Journal of Medicinal Chemistry

Perspective

Table 1. Approved Drugs (2011−2017) Selected for Case Studiesa name

MW

clogP

PSA

po dose (mg)

KD, Ki, EC50, IC50, MIC50; (LE)b

1 2 3 4 5 6 7

tavaborole crisaborole ataluren vortioxetine edaravone tasimelteon lorcaserin

152 251 284 298 174 245 196

1.2 2.6 3.7 4.9 1.3 1.9 3.2

29.5 62.5 76.2 40.6 32.7 38.3 12

topical topical 700 10 60, iv 20 10

IC50 = 1 μM (0.78) IC50 = 0.5 μM (0.47) ICmax = 3 μM (0.38) Ki = 15 nM (0.53) EC50 = 10 μM (0.56) Ki = 0.35 nM (0.74) EC50 = 8 nM (0.88)

8 9 10 11 12

brivaracetam tipiracilg pomalidomide pitolisant dimethyl fumarate migalastat pirfenidone teriflunomide eslicarbazepineh

212 243 273 296 144

1 −0.2 −0.2 4.8 0.8

63.4 89.1 109.6 12.5 52.6

25 6.14 4 18 120

163 185 270 296

−1.4 2.4 2.1 1.5

93 22 73.1 72.6

123 267 14 800

IC50 IC50 IC50 IC50

compound

13 14 15 16

Vd (L)

T1/2 (h)

urine excretion (%)f

target class

2−6 66 4.5−6 1−4 11

50 59 70−90 80 92

IC50 = 100 nM (0.66) Ki = 20 nM (0.68) IC50 = 3 μM (0.4) EC50 = 1.5 nM (0.62) IC50 = 10 μM (0.72)

lowe 2600 18.5 56−126 CSF, CNSc 35d 333 62−138 lowe 53−73

enzyme enzyme unknown GPCR unknown GPCR GPCR

9 2 10 10−12 1

95 27 73 63 16

transporter enzyme multiple GPCR unknown

830 μM (0.42) 1.6 mM (0.30) 0.3 μM (0.49) 30 μM (0.35)

77−133 59−71 11 61d

3−5 3 18−19 d 13−20

77 80 23 90

enzyme enzyme enzyme ion channel

= = = =

a

po dose, Vd, T1/2, and % urine excretion taken from FDA or EMA product labels. bLigand efficiency (LE) for each drug was calculated using corresponding primary potency data in an in vitro assay described in the literature. cDistribution to CSF and CNS described. dBased on 70 kg body weight. eBased on no or moderate distribution to red blood cells and high plasma protein binding. fPercent urine excretion of parent and/or metabolites. gAs part of a combination. hMeasurements of parent drug.



CASE STUDIES: DRUGS DERIVED FROM LEAD-LIKE SCREENING EFFORTS Tavaborole (1, Kerydin).

weight below 300, accounting for 36% of highly prescribed low molecular weight drugs. Of note, the large proportion of approved drugs above molecular weight 440 from 2011 to 2016 differs from the small proportion of drugs above molecular weight 440 represented in the top 200 prescribed drugs. The relative proportions of approved drugs less than molecular weight 300, molecular weight 300−440, and greater than molecular weight 440 differ considerably when comparing drugs approved in 2011−2016 and the top 200 prescribed drugs. A large proportion of drugs below molecular weight 300 are found in the top 200 prescribed drugs, while a higher proportion of drugs above molecular weight 440 are seen in the distribution of drugs approved between 2011 and 2016. Many of the top 200 prescribed low molecular weight drugs have been on the market for a long period of time; it will be interesting to observe how the molecular weight distribution of highly prescribed drugs evolves over time given the molecular weight distribution of recent approvals. For the year 2017, the recent trend of larger proportion of higher molecular weight drugs continues as 18/28 FDA approved drugs have molecular weight of >440 while 3/28 have molecular weight of 10 and an unbound efficacious concentration (Ceff) of > 1 μM fall into a property space of MW < 300 and higher polarity (clogP < 2.5).79 Recommendations and Considerations. Ensure Use of Screening Sets below MW 300. Lead-like starting points are key to the discovery of lead-like drugs. However, recent analyses of commercially available screening compounds observed a median molecular weight of 387 with a median absolute deviation of 78, indicating that a large portion of commercially available screening sets would not cover chemical space below molecular weight 300.80 This implies that many screening efforts likely may not lead to the identification of hits below molecular weight 300, a range representing approximately half of human drug−target pairs. Fortuitously, with the increased focus on fragment-based drug discovery, there are an increasing number of commercially available lead-like starting points.81−83 Lead-like starting points have also been used to identify adipocyte differentiation promoters through phenotypic screening and fragment-based chemical proteomics, providing lead-like starting points for optimization.84 Related to the discovery of lead-like drugs is the “medicinal chemist’s eye”, discussed in a comparative hit triage across many medicinal chemists.85 Namely, this investigation showed that medicinal chemists are relatively biased and differentiated in their evaluation of hit lists and hit triage. When applied to lead-like drug discovery, there may be a bias at the initial stages of screening that eliminate hits too soon based on molecular weight. It is reassuring to see that in the 16 examples included in this review, diverse target classes such as enzymes, GPCRs, ion channels, and transporters are represented. Additionally recent investigations of cryptic and secondary pockets86−88 may extend the target space of lead-like compounds to less

eslicarbazepine acetate) was shown to have the highest sodium channel inhibitory activity in vitro and in vivo. Although no crystal structure of the human sodium channel receptor has been solved, subtype nonselective sodium channel modulators such as eslicarbazepine (16c) are hypothesized to bind to an intracellular site within the sodium channel pore.



DISCUSSION Inspection of the of the 28 of 174 lead-like drugs approved between 2011 and 2017 (see Supporting Information) reveals a property profile that falls between the “rule-of-three” and “rule of five” (Figure 2). For instance, 21 of 28 drugs below 300 MW have calculated log P values of 3 or less, 20 of 28 have three or less hydrogen bond donors, and 18 of 28 have three or less hydrogen bond acceptors. When viewed through the “ruleof-five” lens, only 1 of 28 drugs is an outlier with six hydrogen bonds.58 Additionally, all the lead-like drugs from 2011 to 2017 have total polar surface areas below 125 Å and rotatable bond counts below 10, compliant with property ranges preferable for oral bioavailability.59 There are three common themes that emerge when considering the selected case studies of recently approved drugs below molecular weight 300. First, a portion of the list such as tavaborole, crisaborole, and ataluren originate from screening efforts using lead-like libraries. Second, a large portion of the recently approved drugs are based on medicinal chemistry optimization or derivatization of known lead-like drugs or natural products. For instance, lorcaserin is a conformationally restricted analog of known serotonergic scaffolds, tasimelteon is related to known melatonin receptor agonists, and pomalidomide is a close structural analog of thalidomide. Taken together, the molecular weight of the medicinal chemistry starting point has a strong influence on the molecular weight of the approved drug.60,61 Third, repurposed drugs (dimethyl fumarate, pirfenidone) and active metabolites make up a small but significant portion of the recently approved lead-like drugs. Of note, while the lead-like drugs from the case studies have a range of potency values (Table 1), 11 of 15 have ligand efficiency values above the typical drug (0.45), correlating to high potency for each atom of the drug.62 Lead-like ADME and Toxicity. Lead-like drugs do not differ significantly in physicochemical properties of known drugs. In the case studies included, clogP and polar surface areas are typical of successful, orally bioavailable drugs (see Table 1).63 As a consequence of low molecular weight, the ligand efficiencies (LE) of these drugs are on the higher end of the range. However, the dose ranges of these drugs are within the ranges of historic drugs.64 To investigate ADME implications of molecular weight further, we analyzed data from a collection of 105 drugs of various molecular weights.65 We found that 76% of drugs with less than 300 MW had human oral bioavailability greater than 90% while only 53% of drugs with 300−440 MW and 42% of drugs with MW above 440 had human oral bioavailability greater than 90%. This trend of increased bioavailability of lead-like compounds may correspond to previously noted relationships of solubility and permeability to molecular weight. Specifically, solubility tends to decrease in relation to increased molecular size, and low molecular weight compounds may have both paracellular and transcellular paths to absorption.66−69 Likewise, an analysis of a diverse set of 47 018 compounds by Johnson and colleagues demonstrated the predictive value F

DOI: 10.1021/acs.jmedchem.8b00407 J. Med. Chem. XXXX, XXX, XXX−XXX

Journal of Medicinal Chemistry

Perspective

represented target classes through allostery, induced fit, or other mechanisms. Emphasize MW-Neutral, MW-Limited Medicinal Chemistry. Molecules at or below molecular weight 300 may typically be viewed as hits that need to be advanced through medicinal chemistry optimization to provide drugs, whether through potency or property optimization. However, a comparison of molecular weight ranges of starting compounds to optimized compounds in the monoamine GPCR family identified a median change in molecular weight of 30 (mean change 50) in optimization of starting compounds below molecular weight 300. 89 This implies that optimized compounds can have similar molecular weights compared to their lead-like starting points. The analysis identified ion channels and transporters, in addition to monoamine GPCRs, as protein target classes associated with lower molecular weight ligands. Supporting this, clinical candidates in the ion channel target class have significantly lower molecular mass on average. This implies that starting compounds in certain target classes may be more amenable to optimization efforts toward clinical candidates that retain a low molecular weight.90 Additionally, the identification of binding pockets with lower surface area may also indicate the opportunity for lead-like drug discovery efforts.91 The implementation of lead-like screening strategies is feasible, and lead-like medicinal chemistry optimization efforts, while challenging, may yield desired results. High concentration fragment screening using biochemical assays has demonstrated utility, providing sensitive hit identification with an acceptable number of artifacts due to the high concentration.92 Fragment-based screening in biochemical assays was shown to be suitable for the identification of serine protease and metalloprotease targets and can reduce protein requirements when compared to SPR and NMR fragmentbased screening techniques.93 Keeping molecular weight low during fragment to lead medicinal chemistry efforts can be difficult. An analysis of 2015 and 2016 fragment to lead medicinal chemistry publications by Johnson and colleagues shows that out of 55 publications with hits less than molecular weight 300, only 6 resulted in leads with less than molecular weight 300.94,95 Application of medicinal chemistry strategies such as conformational restriction, heavy atom substitution, and truncation that maintain or reduce molecular weight is advisible to investigate, while strategies such as growth into additional pockets, fragment linking, or appending solubilizing groups may be contraindicated. Further work is needed to determine how and when lead-like screening and medicinal chemistry strategies are deployed. Evaluate Biological and Clinical Options. Finally, sources of drugs without the need of large-scale screening or extensive medicinal chemistry efforts should not be ignored. Metabolites of known drugs, biological investigations of natural products, and clinically driven repurposing of known drugs have all resulted in new drug approvals.

like space may include reduced toxicity and improved absorption and clearance. However, the identification of low molecular weight starting points may be limited by the availability of suitable low molecular weight screening sets. To increase the discovery rate of lead-like drugs, we suggest an increased focus on inclusion of lead-like starting points in screening libraries. While it may not be possible to generate clinical candidates and drugs that are lead-like in all cases, historic and recent drug approvals indicate that this area is ripe for further pursuit.



ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jmedchem.8b00407. List of FDA and EMA approved drugs from 2011 to 2017 with physicochemical properties and Smiles (CSV)



AUTHOR INFORMATION

Corresponding Authors

*B.R.: e-mail: [email protected]. Current Address: Alkermes, Inc. 852 Winter Street, Waltham, Massachusetts 02451. *S.K.B.: e-mail: samit.k.bhattacharya@pfizer.com. ORCID

Samit K. Bhattacharya: 0000-0002-0948-4254 Notes

The authors declare no competing financial interest. Biographies Brian Raymer earned a B.A in Chemistry from St. Olaf College (Northfield, MN) and then joined the Process Development group at Pfizer (Groton, CT). He then moved to Harvard University and obtained his Ph.D. with Prof. David A. Evans working on synthetic routes toward a marine toxin, azaspiracid. In 2004, he joined the Global Discovery Chemistry department at Novartis (Cambridge, MA) working on small molecule projects in the diabetes, metabolism, and cardiovascular disease areas as well as peptide, polymer, and antibody−drug conjugate projects as part of the Chemical Biology department. In 2013 he moved to Pfizer (Cambridge, MA) as a medicinal chemistry and research project leader in Medicinal Design, contributing to two clinical candidates including a ketohexokinase inhibitor currently in phase 2 clinical trials. Samit K. Bhattacharya did his undergraduate work in Organic Chemistry at Jadavpur University, Calcutta, India, and earned his Ph.D. at University of Pennsylvania, Philadelphia, PA, under the tutelage of Professor Jeffrey Winkler working on the total synthesis of anticancer drug paclitaxel (Taxol). Thereafter he moved to Columbia University, NY, as a Postdoctoral Fellow with Professor Samuel Danishefsky, working on the total synthesis of anticancer agent eleutherobin and ganglioside GM-1. In 1999, he joined the Oncology group in Pfizer, Groton, CT, as a medicinal chemist and then moved onto the Cardiovascular and Metabolic Diseases group. Samit has contributed to multiple clinical candidates across these two therapeutic areas. Samit is currently in the Inflammation and Immunology group located in Cambridge, MA.



CONCLUSIONS Compounds below molecular weight 300 represent a fruitful area of drug research with a number of recent drug approvals occurring in this space. Many of these recent drugs are close analogs of previously known drugs or natural substrates, with a key focus of medicinal chemistry optimization being the choice of a low molecular weight starting point and maintaining low molecular weight during the optimization. Advantages of lead-



ACKNOWLEDGMENTS The authors are indebted to Drs. David Price, Markus Boehm, John Litchfield, Fabien Vincent, Phil Carpino, and Manthena Varma for critically reviewing the manuscript and providing G

DOI: 10.1021/acs.jmedchem.8b00407 J. Med. Chem. XXXX, XXX, XXX−XXX

Journal of Medicinal Chemistry

Perspective

(15) Edaravone Acute Infarction Study Group.. Effect of a Novel Free Radical Scavenger, Edaravone (MCI-186), on Acute Brain Infarction. Randomized, Placebo-Controlled, Double-Blind Study at Multicenters. Cerebrovasc. Dis. 2003, 15 (3), 222−229. (16) Abe, K.; Yuki, S.; Kogure, K. Strong Attenuation of Ischemic and Postischemic Brain Edema in Rats by a Novel Free Radical Scavenger. Stroke 1988, 19 (4), 480−485. (17) Brune, K. The Early History of Non-Opioid Analgesics. Acute Pain 1997, 1 (1), 33−40. (18) Hardeland, R. Investigational Melatonin Receptor Agonists. Expert Opin. Invest. Drugs 2010, 19 (6), 747−764. (19) Vachharajani, N. N.; Yeleswaram, K.; Boulton, D. W. Preclinical Pharmacokinetics and Metabolism of BMS-214778, a Novel Melatonin Receptor Agonist. J. Pharm. Sci. 2003, 92 (4), 760−772. (20) Halford, J. C.; Harrold, J. A.; Lawton, C. L.; Blundell, J. E. Serotonin (5-HT) Drugs: Effects on Appetite Expression and Use for the Treatment of Obesity. Curr. Drug Targets 2005, 6 (2), 201−213. (21) Smith, B. M.; Smith, J. M.; Tsai, J. H.; Schultz, J. A.; Gilson, C. A.; Estrada, S. A.; Chen, R. R.; Park, D. M.; Prieto, E. B.; Gallardo, C. S.; Sengupta, D.; Dosa, P. I.; Covel, J. A.; Ren, A.; Webb, R. R.; Beeley, N. R.; Martin, M.; Morgan, M.; Espitia, S.; Saldana, H. R.; Bjenning, C.; Whelan, K. T.; Grottick, A. J.; Menzaghi, F.; Thomsen, W. J. Discovery and Structure-Activity Relationship of (1R)-8-Chloro2,3,4,5-Tetrahydro-1-Methyl-1h-3-Benzazepine (Lorcaserin), a Selective Serotonin 5-HT2c Receptor Agonist for the Treatment of Obesity. J. Med. Chem. 2008, 51 (2), 305−313. (22) Brashier, D. B.; Sharma, A. K.; Dahiya, N.; Singh, S. K.; Khadka, A. Lorcaserin: A Novel Antiobesity Drug. J. Pharmacol. Pharmacother. 2014, 5 (2), 175−178. (23) Rogawski, M. A. Brivaracetam: A Rational Drug Discovery Success Story. Br. J. Pharmacol. 2008, 154 (8), 1555−1557. (24) Klitgaard, H.; Matagne, A.; Nicolas, J. M.; Gillard, M.; Lamberty, Y.; De Ryck, M.; Kaminski, R. M.; Leclercq, K.; Niespodziany, I.; Wolff, C.; Wood, M.; Hannestad, J.; Kervyn, S.; Kenda, B. Brivaracetam: Rationale for Discovery and Preclinical Profile of a Selective SV2A Ligand for Epilepsy Treatment. Epilepsia 2016, 57 (4), 538−548. (25) Russo, E.; Citraro, R.; Mula, M. The Preclinical Discovery and Development of Brivaracetam for the Treatment of Focal Epilepsy. Expert Opin. Drug Discovery 2017, 12 (11), 1169−1178. (26) Weinberg, B. A.; Marshall, J. L.; Salem, M. E. Trifluridine/ Tipiracil and Regorafenib: New Weapons in the War against Metastatic Colorectal Cancer. Clin Adv. Hematol. Oncol. 2016, 14 (8), 630−638. (27) Fukushima, M.; Suzuki, N.; Emura, T.; Yano, S.; Kazuno, H.; Tada, Y.; Yamada, Y.; Asao, T. Structure and Activity of Specific Inhibitors of Thymidine Phosphorylase to Potentiate the Function of Antitumor 2′-Deoxyribonucleosides. Biochem. Pharmacol. 2000, 59 (10), 1227−1236. (28) Peters, G. J.; De Bruin, M.; Fukushima, M.; Van Triest, B.; Hoekman, K.; Pinedo, H. M.; Ackland, S. P. Thymidine Phosphorylase in Angiogenesis and Drug Resistance. Homology with Platelet-Derived Endothelial Cell Growth Factor. Adv. Exp. Med. Biol. 2000, 486, 291−294. (29) Takao, S.; Akiyama, S. I.; Nakajo, A.; Yoh, H.; Kitazono, M.; Natsugoe, S.; Miyadera, K.; Fukushima, M.; Yamada, Y.; Aikou, T. Suppression of Metastasis by Thymidine Phosphorylase Inhibitor. Cancer Res. 2000, 60 (19), 5345−5348. (30) Cleary, J. M.; Rosen, L. S.; Yoshida, K.; Rasco, D.; Shapiro, G. I.; Sun, W. A Phase 1 Study of the Pharmacokinetics of Nucleoside Analog Trifluridine and Thymidine Phosphorylase Inhibitor Tipiracil (Components of TAS-102) Vs Trifluridine Alone. Invest. New Drugs 2017, 35 (2), 189−197. (31) Rios-Tamayo, R.; Martin-Garcia, A.; Alarcon-Payer, C.; Sanchez-Rodriguez, D.; de la Guardia, A.; Garcia Collado, C. G.; Jimenez Morales, A.; Jurado Chacon, M.; Cabeza Barrera, J. Pomalidomide in the Treatment of Multiple Myeloma: Design, Development and Place in Therapy. Drug Des. Dev. Ther. 2017, 11, 2399−2408.

invaluable suggestions. The authors also thank the ACS reviewers for their thoughtful suggestions.



ABBREVIATIONS USED MW, molecular weight; PSA, polar surface area; Ro3, rule of three; LE, ligand efficiency; DHODH, dihydroorotate dehydrogenase; NSAID, nonsteroidal anti-inflammatory drug; HBD, hydrogen bond donor count; SERT, serotonin transporter; ALS, amyotrophic lateral sclerosis; ADME, absorption, distribution, metabolism and excretion



REFERENCES

(1) 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. (2) Lipinski, C. A. Lead- and Drug-Like Compounds: The Rule-ofFive Revolution. Drug Discovery Today: Technol. 2004, 1 (4), 337− 341. (3) Congreve, M.; Carr, R.; Murray, C.; Jhoti, H. A ‘Rule of Three’ for Fragment-Based Lead Discovery? Drug Discovery Today 2003, 8 (19), 876−877. (4) Quinton, J.; Charruault, L.; Nevers, M. C.; Volland, H.; Dognon, J. P.; Creminon, C.; Taran, F. Toward the Limits of Sandwich Immunoassay of Very Low Molecular Weight Molecules. Anal. Chem. 2010, 82 (6), 2536−2540. (5) The Top 200 of 2017. http://clincalc.com/DrugStats/ Top200Drugs.aspx (accessed November 17, 2017). (6) Flick, A. C.; Ding, H. X.; Leverett, C. A.; Kyne, R. E., Jr.; Liu, K. K.; Fink, S. J.; O’Donnell, C. J. Synthetic Approaches to the 2014 New Drugs. Bioorg. Med. Chem. 2016, 24 (9), 1937−1980. (7) Elewski, B. E.; Aly, R.; Baldwin, S. L.; Gonzalez Soto, R. F.; Rich, P.; Weisfeld, M.; Wiltz, H.; Zane, L. T.; Pollak, R. Efficacy and Safety of Tavaborole Topical Solution, 5%, a Novel Boron-Based Antifungal Agent, for the Treatment of Toenail Onychomycosis: Results from 2 Randomized Phase-III Studies. J. Am. Acad. Dermatol. 2015, 73 (1), 62−69. (8) Benkovic, S. J.; Baker, S. J.; Alley, M. R.; Woo, Y. H.; Zhang, Y. K.; Akama, T.; Mao, W.; Baboval, J.; Rajagopalan, P. T.; Wall, M.; Kahng, L. S.; Tavassoli, A.; Shapiro, L. Identification of Borinic Esters as Inhibitors of Bacterial Cell Growth and Bacterial Methyltransferases, Ccrm and Menh. J. Med. Chem. 2005, 48 (23), 7468−7476. (9) Baker, S. J.; Zhang, Y. K.; Akama, T.; Lau, A.; Zhou, H.; Hernandez, V.; Mao, W.; Alley, M. R.; Sanders, V.; Plattner, J. J. Discovery of a New Boron-Containing Antifungal Agent, 5-Fluoro1,3-Dihydro-1-Hydroxy-2,1- Benzoxaborole (AN2690), for the Potential Treatment of Onychomycosis. J. Med. Chem. 2006, 49 (15), 4447−4450. (10) Paton, D. M. Crisaborole: Phosphodiesterase Inhibitor for Treatment of Atopic Dermatitis. Drugs Today 2017, 53 (4), 239−245. (11) Pibiri, I.; Lentini, L.; Melfi, R.; Gallucci, G.; Pace, A.; Spinello, A.; Barone, G.; Di Leonardo, A. Enhancement of Premature Stop Codon Readthrough in the Cftr Gene by Ataluren (PTC124) Derivatives. Eur. J. Med. Chem. 2015, 101, 236−244. (12) Lentini, L.; Melfi, R.; Di Leonardo, A.; Spinello, A.; Barone, G.; Pace, A.; Palumbo Piccionello, A.; Pibiri, I. Toward a Rationale for the Ptc124 (Ataluren) Promoted Readthrough of Premature Stop Codons: A Computational Approach and GFP-Reporter Cell-Based Assay. Mol. Pharmaceutics 2014, 11 (3), 653−664. (13) Karp, G. M.; Hwang, S.; Chen, G.; Almstead, N. G.; Moon, Y.C. 1,2,4-Oxadiazole Benzoic Acid Compounds and Their Use for Nonsense Suppression and the Treatment of Disease. US6992096B2, 2004. (14) Bang-Andersen, B.; Ruhland, T.; Jorgensen, M.; Smith, G.; Frederiksen, K.; Jensen, K. G.; Zhong, H.; Nielsen, S. M.; Hogg, S.; Mork, A.; Stensbol, T. B. Discovery of 1-[2-(2,4Dimethylphenylsulfanyl)Phenyl]Piperazine (Lu AA21004): A Novel Multimodal Compound for the Treatment of Major Depressive Disorder. J. Med. Chem. 2011, 54 (9), 3206−3221. H

DOI: 10.1021/acs.jmedchem.8b00407 J. Med. Chem. XXXX, XXX, XXX−XXX

Journal of Medicinal Chemistry

Perspective

(32) Muller, G. W.; Corral, L. G.; Shire, M. G.; Wang, H.; Moreira, A.; Kaplan, G.; Stirling, D. I. Structural Modifications of Thalidomide Produce Analogs with Enhanced Tumor Necrosis Factor Inhibitory Activity. J. Med. Chem. 1996, 39 (17), 3238−3240. (33) D’Amato, R. J.; Lentzsch, S.; Anderson, K. C.; Rogers, M. S. Mechanism of Action of Thalidomide and 3-Aminothalidomide in Multiple Myeloma. Semin. Oncol. 2001, 28 (6), 597−601. (34) Schwartz, J. C. The Histamine H3 Receptor: From Discovery to Clinical Trials with Pitolisant. Br. J. Pharmacol. 2011, 163 (4), 713−721. (35) Ropper, A. H. The “Poison Chair” Treatment for Multiple Sclerosis. N. Engl. J. Med. 2012, 367 (12), 1149−1150. (36) Held, K. D.; Epp, E. R.; Clark, E. P.; Biaglow, J. E. Effect of Dimethyl Fumarate on the Radiation Sensitivity of Mammalian Cells in Vitro. Radiat. Res. 1988, 115 (3), 495−502. (37) Schimrigk, S.; Brune, N.; Hellwig, K.; Lukas, C.; Bellenberg, B.; Rieks, M.; Hoffmann, V.; Pohlau, D.; Przuntek, H. Oral Fumaric Acid Esters for the Treatment of Active Multiple Sclerosis: An Open-Label, Baseline-Controlled Pilot Study. Eur. J. Neurol 2006, 13 (6), 604− 610. (38) Gold, R.; Kappos, L.; Arnold, D. L.; Bar-Or, A.; Giovannoni, G.; Selmaj, K.; Tornatore, C.; Sweetser, M. T.; Yang, M.; Sheikh, S. I.; Dawson, K. T. Investigators, D. S. Placebo-Controlled Phase 3 Study of Oral BG-12 for Relapsing Multiple Sclerosis. N. Engl. J. Med. 2012, 367 (12), 1098−1107. (39) McGuire, V. A.; Ruiz-Zorrilla Diez, T.; Emmerich, C. H.; Strickson, S.; Ritorto, M. S.; Sutavani, R. V.; Weiss, A.; Houslay, K. F.; Knebel, A.; Meakin, P. J.; Phair, I. R.; Ashford, M. L.; Trost, M.; Arthur, J. S. Dimethyl Fumarate Blocks Pro-Inflammatory Cytokine Production Via Inhibition of TLR Induced M1 and K63 Ubiquitin Chain Formation. Sci. Rep. 2016, 6, 31159. (40) Susitaival, P.; Winhoven, S. M.; Williams, J.; Lammintausta, K.; Hasan, T.; Beck, M. H.; Gruvberger, B.; Zimerson, E.; Bruze, M. An Outbreak of Furniture Related Dermatitis (“Sofa Dermatitis”) in Finland and the UK: History and Clinical Cases. J. Eur. Acad. Dermatol. Venereol. 2010, 24 (4), 486−489. (41) Miyake, Y.; Ebata, M. Galactostatin, a New Beta-Galactosidase Inhibitor from Streptomyces Lydicus. J. Antibiot. 1987, 40 (1), 122− 123. (42) Sanchez-Fernandez, E. M.; Garcia Fernandez, J. M.; Mellet, C. O. Glycomimetic-Based Pharmacological Chaperones for Lysosomal Storage Disorders: Lessons from Gaucher, GM1-Gangliosidosis and Fabry Diseases. Chem. Commun. (Cambridge, U. K.) 2016, 52 (32), 5497−5515. (43) Khanna, R.; Soska, R.; Lun, Y.; Feng, J.; Frascella, M.; Young, B.; Brignol, N.; Pellegrino, L.; Sitaraman, S. A.; Desnick, R. J.; Benjamin, E. R.; Lockhart, D. J.; Valenzano, K. J. The Pharmacological Chaperone 1-Deoxygalactonojirimycin Reduces Tissue Globotriaosylceramide Levels in a Mouse Model of Fabry Disease. Mol. Ther. 2010, 18 (1), 23−33. (44) Benjamin, E. R.; Flanagan, J. J.; Schilling, A.; Chang, H. H.; Agarwal, L.; Katz, E.; Wu, X.; Pine, C.; Wustman, B.; Desnick, R. J.; Lockhart, D. J.; Valenzano, K. J. The Pharmacological Chaperone 1Deoxygalactonojirimycin Increases Alpha-Galactosidase a Levels in Fabry Patient Cell Lines. J. Inherited Metab. Dis. 2009, 32 (3), 424− 440. (45) Abian, O.; Alfonso, P.; Velazquez-Campoy, A.; Giraldo, P.; Pocovi, M.; Sancho, J. Therapeutic Strategies for Gaucher Disease: Miglustat (NB-DNJ) as a Pharmacological Chaperone for Glucocerebrosidase and the Different Thermostability of Velaglucerase Alfa and Imiglucerase. Mol. Pharmaceutics 2011, 8 (6), 2390−2397. (46) Schiffmann, R. Agalsidase Treatment for Fabry Disease: Uses and Rivalries. Genet. Med. 2010, 12 (11), 684−685. (47) Schiffmann, R.; Martin, R. A.; Reimschisel, T.; Johnson, K.; Castaneda, V.; Lien, Y. H.; Pastores, G. M.; Kampmann, C.; Ries, M.; Clarke, J. T. Four-Year Prospective Clinical Trial of Agalsidase Alfa in Children with Fabry Disease. J. Pediatr. 2010, 156 (5), 832−837. (48) Benjamin, E. R.; Della Valle, M. C.; Wu, X.; Katz, E.; Pruthi, F.; Bond, S.; Bronfin, B.; Williams, H.; Yu, J.; Bichet, D. G.; Germain, D.

P.; Giugliani, R.; Hughes, D.; Schiffmann, R.; Wilcox, W. R.; Desnick, R. J.; Kirk, J.; Barth, J.; Barlow, C.; Valenzano, K. J.; Castelli, J.; Lockhart, D. J. The Validation of Pharmacogenetics for the Identification of Fabry Patients to Be Treated with Migalastat. Genet. Med. 2017, 19 (4), 430−438. (49) Antoniu, S. A. Pirfenidone for the Treatment of Idiopathic Pulmonary Fibrosis. Expert Opin. Invest. Drugs 2006, 15 (7), 823− 828. (50) Genentech Mission Critical. https://www.gene.com/stories/ mission-critical (accessed November 17, 2017). (51) Nathan, S. D.; Albera, C.; Bradford, W. Z.; Costabel, U.; Glaspole, I.; Glassberg, M. K.; Kardatzke, D. R.; Daigl, M.; Kirchgaessler, K.-U.; Lancaster, L. H.; Lederer, D. J.; Pereira, C. A.; Swigris, J. J.; Valeyre, D.; Noble, P. W. Effect of Pirfenidone on Mortality: Pooled Analyses and Meta-Analyses of Clinical Trials in Idiopathic Pulmonary Fibrosis. Lancet Respir. Med. 2017, 5 (1), 33− 41. (52) Noble, P. W.; Albera, C.; Bradford, W. Z.; Costabel, U.; Glassberg, M. K.; Kardatzke, D.; King, T. E.; Lancaster, L.; Sahn, S. A.; Szwarcberg, J.; Valeyre, D.; du Bois, R. M. Pirfenidone in Patients with Idiopathic Pulmonary Fibrosis (Capacity): Two Randomised Trials. Lancet 2011, 377 (9779), 1760−1769. (53) Macias-Barragan, J.; Sandoval-Rodriguez, A.; Navarro-Partida, J.; Armendariz-Borunda, J. The Multifaceted Role of Pirfenidone and Its Novel Targets. Fibrog. Tissue Repair 2010, 3, 16. (54) Nanthakumar, C. B.; Hatley, R. J.; Lemma, S.; Gauldie, J.; Marshall, R. P.; Macdonald, S. J. Dissecting Fibrosis: Therapeutic Insights from the Small-Molecule Toolbox. Nat. Rev. Drug Discovery 2015, 14 (10), 693−720. (55) Bertolini, G.; Aquino, M.; Biffi, M.; d’Atri, G.; Di Pierro, F.; Ferrario, F.; Mascagni, P.; Somenzi, F.; Zaliani, A.; Leoni, F. A New Rational Hypothesis for the Pharmacophore of the Active Metabolite of Leflunomide, a Potent Immunosuppressive Drug. J. Med. Chem. 1997, 40 (13), 2011−2016. (56) Munier-Lehmann, H.; Vidalain, P. O.; Tangy, F.; Janin, Y. L. On Dihydroorotate Dehydrogenases and Their Inhibitors and Uses. J. Med. Chem. 2013, 56 (8), 3148−3167. (57) Benes, J.; Parada, A.; Figueiredo, A. A.; Alves, P. C.; Freitas, A. P.; Learmonth, D. A.; Cunha, R. A.; Garrett, J.; Soares-da-Silva, P. Anticonvulsant and Sodium Channel-Blocking Properties of Novel 10,11-Dihydro-5h-Dibenz[b,f]Azepine-5-Carboxamide Derivatives. J. Med. Chem. 1999, 42 (14), 2582−2587. (58) Droxidopa, a prodrug, has six hydrogen bond donors, but the active entity, the neurotransmitter, norepinephrine has five and therefore is not an outlier to the rule-of-five concept. (59) Veber, D. F.; Johnson, S. R.; Cheng, H. Y.; Smith, B. R.; Ward, K. W.; Kopple, K. D. Molecular Properties That Influence the Oral Bioavailability of Drug Candidates. J. Med. Chem. 2002, 45 (12), 2615−2623. (60) Leeson, P. D. Molecular Inflation, Attrition and the Rule of Five. Adv. Drug Delivery Rev. 2016, 101, 22−33. (61) Hopkins, A. L.; Groom, C. R.; Alex, A. Ligand Efficiency: A Useful Metric for Lead Selection. Drug Discovery Today 2004, 9 (10), 430−431. (62) Hopkins, A. L.; Keseru, G. M.; Leeson, P. D.; Rees, D. C.; Reynolds, C. H. The Role of Ligand Efficiency Metrics in Drug Discovery. Nat. Rev. Drug Discovery 2014, 13 (2), 105−121. (63) Egan, W. J.; Merz, K. M., Jr.; Baldwin, J. J. Prediction of Drug Absorption Using Multivariate Statistics. J. Med. Chem. 2000, 43 (21), 3867−3877. (64) Chmielewska, A.; Lamparczyk, H. Mass Versus Molar Doses, Similarities and Differences. Pharmazie 2008, 63 (11), 843−848. (65) Varma, M. V.; Gardner, I.; Steyn, S. J.; Nkansah, P.; Rotter, C. J.; Whitney-Pickett, C.; Zhang, H.; Di, L.; Cram, M.; Fenner, K. S.; ElKattan, A. F. Ph-Dependent Solubility and Permeability Criteria for Provisional Biopharmaceutics Classification (BCS and BDDCS) in Early Drug Discovery. Mol. Pharmaceutics 2012, 9 (5), 1199−1212. I

DOI: 10.1021/acs.jmedchem.8b00407 J. Med. Chem. XXXX, XXX, XXX−XXX

Journal of Medicinal Chemistry

Perspective

(87) Ludlow, R. F.; Verdonk, M. L.; Saini, H. K.; Tickle, I. J.; Jhoti, H. Detection of Secondary Binding Sites in Proteins Using Fragment Screening. Proc. Natl. Acad. Sci. U. S. A. 2015, 112 (52), 15910− 15915. (88) Hart, K. M.; Moeder, K. E.; Ho, C. M. W.; Zimmerman, M. I.; Frederick, T. E.; Bowman, G. R. Designing Small Molecules to Target Cryptic Pockets Yields Both Positive and Negative Allosteric Modulators. PLoS One 2017, 12 (6), e0178678. (89) Morphy, R. The Influence of Target Family and Functional Activity on the Physicochemical Properties of Pre-Clinical Compounds. J. Med. Chem. 2006, 49 (10), 2969−2978. (90) Waring, M. J.; Arrowsmith, J.; Leach, A. R.; Leeson, P. D.; Mandrell, S.; Owen, R. M.; Pairaudeau, G.; Pennie, W. D.; Pickett, S. D.; Wang, J.; Wallace, O.; Weir, A. An Analysis of the Attrition of Drug Candidates from Four Major Pharmaceutical Companies. Nat. Rev. Drug Discovery 2015, 14 (7), 475−486. (91) Cheng, A. C.; Coleman, R. G.; Smyth, K. T.; Cao, Q.; Soulard, P.; Caffrey, D. R.; Salzberg, A. C.; Huang, E. S. Structure-Based Maximal Affinity Model Predicts Small-Molecule Druggability. Nat. Biotechnol. 2007, 25 (1), 71−75. (92) Barker, J.; Courtney, S.; Hesterkamp, T.; Ullmann, D.; Whittaker, M. Fragment Screening by Biochemical Assay. Expert Opin. Drug Discovery 2006, 1 (3), 225−236. (93) Boettcher, A.; Ruedisser, S.; Erbel, P.; Vinzenz, D.; Schiering, N.; Hassiepen, U.; Rigollier, P.; Mayr, L. M.; Woelcke, J. FragmentBased Screening by Biochemical Assays: Systematic Feasibility Studies with Trypsin and MMP12. J. Biomol. Screening 2010, 15 (9), 1029− 1041. (94) Johnson, C. N.; Erlanson, D. A.; Murray, C. W.; Rees, D. C. Fragment-to-Lead Medicinal Chemistry Publications in 2015. J. Med. Chem. 2017, 60 (1), 89−99. (95) Johnson, C. N.; Erlanson, D. A.; Jahnke, W.; Mortenson, P. N.; Rees, D. C. Fragment-to-Lead Medicinal Chemistry Publications in 2016. J. Med. Chem. 2018, 61 (5), 1774−1784.

(66) Huuskonen, J.; Livingstone, D. J.; Manallack, D. T. Prediction of Drug Solubility from Molecular Structure Using a Drug-Like Training Set. SAR QSAR Environ. Res. 2008, 19 (3−4), 191−212. (67) Yazdanian, M.; Glynn, S. L.; Wright, J. L.; Hawi, A. Correlating Partitioning and Caco-2 Cell Permeability of Structurally Diverse Small Molecular Weight Compounds. Pharm. Res. 1998, 15 (9), 1490−1494. (68) Palm, K.; Stenberg, P.; Luthman, K.; Artursson, P. Polar Molecular Surface Properties Predict the Intestinal Absorption of Drugs in Humans. Pharm. Res. 1997, 14 (5), 568−571. (69) Artursson, P.; Ungell, A. L.; Lofroth, J. E. Selective Paracellular Permeability in Two Models of Intestinal Absorption: Cultured Monolayers of Human Intestinal Epithelial Cells and Rat Intestinal Segments. Pharm. Res. 1993, 10 (8), 1123−1129. (70) Johnson, T. W.; Dress, K. R.; Edwards, M. Using the Golden Triangle to Optimize Clearance and Oral Absorption. Bioorg. Med. Chem. Lett. 2009, 19 (19), 5560−5564. (71) Rankovic, Z. CNS Physicochemical Property Space Shaped by a Diverse Set of Molecules with Experimentally Determined Exposure in the Mouse Brain. J. Med. Chem. 2017, 60 (14), 5943−5954. (72) Smith, D. A.; Beaumont, K.; Maurer, T. S.; Di, L. Volume of Distribution in Drug Design. J. Med. Chem. 2015, 58 (15), 5691− 5698. (73) Smith, D. A.; Beaumont, K.; Maurer, T. S.; Di, L. Relevance of Half-Life in Drug Design. J. Med. Chem. 2018, 61 (10), 4273−4282. (74) Sharifi, M.; Ghafourian, T. Estimation of Biliary Excretion of Foreign Compounds Using Properties of Molecular Structure. AAPS J. 2014, 16 (1), 65−78. (75) Fleck, C.; Braunlich, H. Factors Determining the Relationship between Renal and Hepatic Excretion of Xenobiotics. Arzneimittelforschung 1990, 40 (8), 942−946. (76) Varma, M. V.; Lai, Y.; El-Kattan, A. F. Molecular Properties Associated with Transporter-Mediated Drug Disposition. Adv. Drug Delivery Rev. 2017, 116, 92−99. (77) Meanwell, N. A. Improving Drug Design: An Update on Recent Applications of Efficiency Metrics, Strategies for Replacing Problematic Elements, and Compounds in Nontraditional Drug Space. Chem. Res. Toxicol. 2016, 29 (4), 564−616. (78) Struck, S.; Schmidt, U.; Gruening, B.; Jaeger, I. S.; Hossbach, J.; Preissner, R. Toxicity Versus Potency: Elucidation of Toxicity Properties Discriminating between Toxins, Drugs, and Natural Compounds. Genome Inform 2008, 20, 231−242. (79) Price, D.; Lee, K. Unpublished results. (80) Zuegg, J.; Cooper, M. A. Drug-likeness and Increased Hydrophobicity of Commercially Available Compound Libraries for Drug Screening. Curr. Top. Med. Chem. 2012, 12 (14), 1500−1513. (81) de Kloe, G. E.; Bailey, D.; Leurs, R.; de Esch, I. J. Transforming Fragments into Candidates: Small Becomes Big in Medicinal Chemistry. Drug Discovery Today 2009, 14 (13−14), 630−646. (82) Chessari, G.; Woodhead, A. J. From Fragment to Clinical Candidate–a Historical Perspective. Drug Discovery Today 2009, 14 (13−14), 668−675. (83) Keseru, G. M.; Erlanson, D. A.; Ferenczy, G. G.; Hann, M. M.; Murray, C. W.; Pickett, S. D. Design Principles for Fragment Libraries: Maximizing the Value of Learnings from Pharma FragmentBased Drug Discovery (FBDD) Programs for Use in Academia. J. Med. Chem. 2016, 59 (18), 8189−8206. (84) Parker, C. G.; Galmozzi, A.; Wang, Y.; Correia, B. E.; Sasaki, K.; Joslyn, C. M.; Kim, A. S.; Cavallaro, C. L.; Lawrence, R. M.; Johnson, S. R.; Narvaiza, I.; Saez, E.; Cravatt, B. F. Ligand and Target Discovery by Fragment-Based Screening in Human Cells. Cell 2017, 168 (3), 527−541. (85) Lajiness, M. S.; Maggiora, G. M.; Shanmugasundaram, V. Assessment of the Consistency of Medicinal Chemists in Reviewing Sets of Compounds. J. Med. Chem. 2004, 47 (20), 4891−4896. (86) Oleinikovas, V.; Saladino, G.; Cossins, B. P.; Gervasio, F. L. Understanding Cryptic Pocket Formation in Protein Targets by Enhanced Sampling Simulations. J. Am. Chem. Soc. 2016, 138 (43), 14257−14263. J

DOI: 10.1021/acs.jmedchem.8b00407 J. Med. Chem. XXXX, XXX, XXX−XXX