Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy 263

Computational Studies of HIV-1 Protease Inhibitors

BY WESLEY SCHAAL

ACTA UNIVERSITATIS UPSALIENSIS UPPSALA 2002 Dissertation for the Degree of Doctor of Philosophy (Faculty of Pharmacy) in Organic Pharmaceutical Chemistry presented at Uppsala University in 2002

ABSTRACT

Schaal, W. 2002. Computational Studies of HIV-1 Protease Inhibitors. Acta Universitatis Upsaliensis. Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy 263. 88 pp. Uppsala. ISBN 91-554-5213-2.

Human Immunodeficiency Virus (HIV) is the causative agent of the pandemic disease Acquired Immune Deficiency Syndrome (AIDS). HIV acts to disrupt the immune system which makes the body susceptible to opportunistic infections. Untreated, AIDS is generally fatal. Twenty years of research by countless scientists around the world has led to the discovery and exploitation of several targets in the replication cycle of HIV. Many lives have been saved, prolonged and improved as a result of this massive effort. One particularly successful target has been the inhibition of HIV protease. In combination with the inhibition of HIV reverse transcriptase, protease inhibitors have helped to reduce viral loads and partially restore the immune system. Unfortunately, viral mutations leading to drug resistance and harmful side- effects of the current medicines have identified the need for new drugs to combat HIV.

This study presents computational efforts to understand the interaction of inhibitors to HIV protease. The first part of this study has used molecular modelling and Comparative Molecular Field Analysis (CoMFA) to help explain the structure-active relationship of a novel series of protease inhibitors. The inhibitors are sulfamide derivatives structurally similar to the cyclic urea candidate drug mozenavir (DMP-450). The central ring of the sulfamides twists to adopt a nonsymmetrical binding mode distinct from that of the cyclic ureas. The energetics of this twist has been studied with ab initio calculations to develop improved empirical force field parameters for use in molecular modelling.

The second part of this study has focused on an analysis of the association and dissociation kinetics of a broad collection of HIV protease inhibitors. Quantitative models have been derived using CoMFA which relate the dissociation rate back to the chemical structures. Efforts have also been made to improve the models by systematically varying the parameters used to generate them.

Keywords: HIV Protease, 3D-QSAR, CoMFA, Molecular Modelling, Force Field Parameterization, Quantum Mechanics, DFT, Enzyme Kinetics.

Wesley Schaal, Organic Pharmaceutical Chemistry, Department of Medicinal Chemistry, Uppsala University, Box 574, SE-751 23 Uppsala, Sweden

© Wesley Schaal 2002

ISSN 0282-7484 ISBN 91-554-5213-2

Printed in Sweden by Uppsala University, Tryck & Medier, Uppsala 2002 To Kaisa, Sonia and Ellen ABBREVIATIONS

3D-QSAR three dimensional quantitative structure-activity relationship AIDS acquired immunodeficiency syndrome B3LYP Becke's 3-parameter exchange and Lee-Yang-Parr correlation functional CADD computer-aided drug design CoMFA comparative molecular field analysis DFT density field theory FF (empirical) force field HIV human immunodeficiency virus IN integrase

Ki inhibitory constant kon association rate koff dissociation rate logkon log10(kon) NNRTI non-nucleoside reverse transcriptase inhibitor NRTI nucleoside reverse transcriptase inhibitor pKi log10(1/Ki) pkoff log10(1/koff) PLS partial least squares or projections to latent structures PR protease PRI protease inhibitor QM quantum mechanics QSAR quantitative structure-activity relationship RT reverse transcriptase SAMPLS sample-distance partial least squares SAR structure-activity relationship TS transition state PAPERS DISCUSSED

This thesis is based on the following papers:

I. Hultén, J.; Andersson, H. O.; Schaal, W.; Danielsson, H. U.; Classon, B.; Kvarnström, I.; Karlén, A.; Unge, T.; Samuelsson, B.; Hallberg, A. Inhibitors of the C2-Symmetric HIV-1 Protease: Nonsymmetric Binding of a Symmetric Cyclic Sulfamide with Ketoxime Groups in the P2/P2' Side Chains. J. Med. Chem. 1999, 42, 4054-4061.

II. Schaal, W.; Karlsson, A.; Ahlsén, G.; Lindberg, J.; Andersson, H. O.; Danielson, U. H.; Classon, B.; Unge, T.; Samuelsson, B.; Hultén, J.; Hallberg, A.; Karlén, A. Synthesis and Comparative Molecular Field Analysis (CoMFA) of Symmetric and Nonsymmetric Cyclic Sulfamide HIV-1 Protease Inhibitors. J. Med. Chem. 2001, 44, 155-169.

III. Hämäläinen, M. D.; Markgren, P.-O.; Schaal, W.; Karlén, A.; Classon, B.; Vrang, L.; Samuelsson, B.; Hallberg, A.; Danielson, U. H. Characterization of a Set of HIV-1 Protease Inhibitors Using Binding Kinetics Data from a Biosensor-Based Screen. J. Biomol. Screen. 2000, 5, 353-360.

IV. Schaal, W.; Markgren, P.-O.; Hämäläinen, M. D.; Danielson, U. H.; Hallberg, A; Karlén, A. Comparative Molecular Field Analysis (CoMFA) of the Association and Dissociation Rate Constants of a Diverse Set of HIV-1 Protease Inhibitors. In manuscript.

Reprints were made with permission from the publishers CONTENTS

1 INTRODUCTION 7 1.1 Etiology of AIDS 7 1.2 Structure of HIV 9 1.3 Replication of HIV 10 1.4 Current Targets and Agents of Anti-HIV Chemotherapy 13 1.5 Results of Anti-HIV Chemotherapy 17 1.6 HIV Protease 21 2 COMPUTATIONAL CHEMISTRY 24 2.1 Quantum Mechanics 24 2.2 Molecular Mechanics 27 2.3 Quantitative Structure-Activity Relationship (QSAR) 28 3 AIMS OF THE PRESENT STUDY 31 4 CYCLIC SULFAMIDE-BASED HIV PROTEASE INHIBITORS 32 4.1 Cyclic Urea-Based Inhibitors 32 4.2 Cyclic Sulfamide-Based Inhibitors 34 4.3 Study of the Ring Flip 35 4.4 Generality of the Ring Flip 38 4.5 Exploitation of the Ring Flip 40 5 KINETIC ANALYSIS OF HIV PROTEASE INHIBITORS 45 5.1 The Technology of Surface Plasmon Resonance Biosensors 45 5.2 An SPR Screen of HIV Protease Inhibitors 47 5.3 Analysis of the Screening Data 49 5.4 Quantitative Structural Analysis of Kinetics Data 50 5.5 Combinatorial CoMFA 52 5.6 Computational Details 54 6 EMPIRICAL FORCE FIELD PARAMETERIZATION 56 7 CONCLUDING REMARKS 62 8 ACKNOWLEDGEMENTS 63 9 REFERENCES 65 1 ACQUIRED IMMUNODEFICIENCY SYNDROME (AIDS)

In mid-1981, five cases of a rare form of pneumonia (Pneumocystis carinii) and severe viral infections in previously healthy young adults was rather quietly reported in Los Angeles.1 Soon, an additional 26 cases of the pneumonia and another unusual disease, Kaposi's sarcoma,2 were discovered in California and New York.3 The disease was accompanied by a depressed immune system and a susceptibility to opportunistic infections. This disease is now known by the name of acquired immunodeficiency syndrome (AIDS).4-6

In the early 1980's, it would have been difficult to anticipate the full scope of AIDS which by December 2001 has claimed the lives of 24.8 million. It has become the leading cause of death in sub-Saharan Africa and fourth worldwide. Today AIDS is recognized as a global epidemic which is not limited to any specific subpopulations. With an estimated 40 million people currently infected and significant increases expected in some areas of the world, AIDS could be classified as one of the worst diseases ever known.7

1.1 ETIOLOGY OF AIDS

The first AIDS patients all had had a history of cytomegalovirus infection so this became the first hypothesis of the origin of the disease8 but this was eventually rejected.9 Other theories surrounded the fact the that the patients also fit a particular demographic: homosexual males. The sexual stimulants amyl- and isobutyl nitrate were implicated as possible etiological agents.10 This too could be quickly disproved after similar cases were discovered in Haiti11 and Africa12 and among hemophiliacs,13 infants14 and women.15 Outside the scientific literature, theories regarding the cause of AIDS have varied wildly to even include divine punishment.16

Beginning with the isolation of a novel retrovirus in 198317 which was later associated with AIDS,18 a clearer picture of the disease began to emerge.9 This virus which has

7 been known as HTLV-III (Human T-cell Leukemia Virus Type 3) and ARV (AIDS related virus) is now known as human immunodeficiency virus (HIV).5,6 After some initial resistance, HIV is generally agreed to be the sole causative agent though some dissent remains in the scientific community even today.19

Two distinct types of HIV have been identified: HIV-1 and HIV-2.20 HIV-1 has been further divided into three virus groups: the predominant M group, which is responsible for most of the epidemic, and N and O.21 The origin of HIV-1 is most likely a cross- species transmission (zoonosis) of a Simian Immunodeficiency Virus (SIV) from a subspecies of Chimpanzee (Pan troglodytes troglodytes)22 but probably from different events for each group.23-25 The date of the zoonosis events have not been precisely discovered but antibodies against group-M HIV-1 were found in a serum sample collected in the Belgian Congo in 1959.26 Models of the genetic divergence of the 11 subtypes of group M date a common ancestor somewhere around 1915-1941.27 HIV-2 is thought to have originated from zoonosis events from sooty mangabeys (Cercocebus atys).28

The primary target of HIV seems to be CD4+ T lymphocytes which are part of the machinery of our immune system.29 The primary phase of HIV infection progresses fairly rapidly and may exhibit mononucleosis-like symptoms within a few weeks.30 During this early phase, the extent of infection is high and virion (virus particle) concentration may exceed a million copies per ml blood.31 The host's immune response usually kicks in after a few weeks and the level of virus in the blood declines to bring HIV infection into its second phase. This long, asymptomatic period characterizes HIV as a lentivirus ("slow virus").32 Viral replication is still active and cells are rapidly being infected and eliminated during this period.33,34 The turnover of T cells gradually leads to a decline in their number.35 In the third and final phase of infection, the number of CD4+ T cells drops more quickly and the viral load increases to produce clinical immunodeficiency.

8 1.2 STRUCTURE OF HIV

Figure 1.1. Schematic of the HIV virion.

The mature HIV virion is an essentially spherical particle with a radius of about 10 nm (Figure 1.1). The virus is surrounded by a lipid bilayer derived from the host cell and contains several cellular membrane proteins.36 The outer portion of this envelope is spotted with surface glycoprotein gp120 (named for its approximate molecular weight) adhered to transmembrane protein gp41. The inside of the envelope is lined with matrix protein p17. Within this shell is the conical capsid core made up of capsid protein p24. The core holds two copies of the single stranded RNA which make up the viral genome. As with all other lentiviruses, HIV is a retrovirus. This means that HIV stores its genetic information as RNA which needs to "reverse-transcribed" to DNA. Accompanying the genome are multiple copies of nucleocapsid protein p7, auxiliary proteins Nef, Vif and Vpr and the essential enzymes: protease, reverse transcriptase and integrase.37 Other auxiliary proteins, e.g. Vpu, Tat and Rev, are not thought to be carried in the virion but are synthesized in the host cell.37,38

9 1.3 REPLICATION OF HIV

A schematic representation of the replication cycle of HIV appears in Figure 1.2. A myriad of cellular machinery is used to augment HIV's special tools.39,40 With over 175 000 articles indexed for HIV and/or AIDS on Medline,41 it is certainly one of the most thoroughly studied systems today. As such, many details of the biology of HIV will be omitted for the sake of brevity.

Figure 1.2. Schematic representation of the replication cycle of HIV.

Virus entry. The entry of HIV into a host cell may be divided into 3 distinct steps: attachment, coreceptor interaction and fusion. Attachment of HIV-1 to the host cell surface is mediated through gp120 on the virion surface binding to a CD4 antigen on the host cell.42 Endogenous CD4 is present on the surface of many lymphocytes, which make up a critical part of the body's immune system. This gp120-CD4 complex interacts with a coreceptor on the cell surface, typically chemokine CXCR4 or

10 CCR5.43 Transmembrane glycoprotein gp41 mediates membrane fusion to complete virus entry into the host cell.

Uncoating the capsid core. Following fusion, the p24 encased capsid core is disrupted to dump the contents into the cytoplasm of the host cell. It seems that this is accomplished with the help of a cytoplasmic peptidyl-prolyl cis-trans isomerase called cyclophilin A (hCyp-18) which had been incorporated into the virion.44,45

Reverse transcription. Successful entry of the contents of the viral capsid core is followed by the reverse transcription of complementary DNA strand from the viral RNA template by the viral enzyme reverse transcriptase (RT) in a complex with other viral proteins.46 RT then degrades the RNA and produces the double-stranded viral DNA. RT is highly error-prone since it is unable to catalyze the proof-reading which a normal DNA polymerase performs.47

Nuclear import. The newly synthesized viral DNA is then imported into the nucleus of the host cell. A short triple-helical region, made from a flap of about 99 bases, synthesized during an interruption of reverse transcription seems to be necessary for this event.48,49 Other viral proteins, such as Vpr,50 are also thought to be involved but the system is complex.

Integration. The properly placed viral DNA is processed and transferred to the host genome by the viral enzyme integrase (IN) as the central agent.46,51,52 Once the viral DNA has been inserted, infection in that cell is for all intents and purposes permanent since finding a way to selectively remove that little patch of DNA from the host genome would seem to be a monumental task.

Transcription and translation. Once the viral DNA has been inserted into the host cell's genome, HIV may persist in a latent, proviral state for many years in unstimulated T cells.53,54 Activation of the host cells results in transcription of the viral DNA by the host cell machinery into messenger RNA (mRNA). Early genes to be

11 activated express auxiliary proteins Tat, Nef, Rev and a few others. Tat acts as a strong promoter of viral transcription,55,56 Nef acts as a weak negative regulator57 and Rev promotes switching to the expression of the structural proteins and enzymes.58 Regulation of viral expression involves a variety of interactions with the cellular proteins.59,60 The auxiliary proteins have also been implicated in other roles such as the down-regulation and degradation of cell-surface CD4 in infected cells by Vpu and Nef, respectively, to promote the release of new virions.61-63

The second phase of transcription produces the unspliced mRNA for the precursor proteins Gag (Pr55gag) and Gag-Pol (Pr180gag), which is the result of a translational frame shifting event, in an approximately 20:1 ratio.64 The unspliced RNA is also intended to be used as the genome of the next generation of the virus. Gag and Gag- Pol are transported out of the cell nucleus by a poorly understood mechanism65 and anchor to the wall through linkage with myristate at their N-termini.66,67

The precursor for the envelope glycoproteins gp120 and gp41 are treated like cellular membrane proteins: synthesized, processed (glycosylated and cleaved) and transported to the cell surface in the endoplasmic reticulum and the golgi apparatus68-70 though some interaction with the Gag precursor has been implicated.71

Production of a new virion. Assembly of a new virus particle begins at the cell surface with the clustering of roughly 2000 Gag proteins, 200 Gag-Pol proteins, processed envelope proteins gp120 and gp41, two copies of the viral genomic RNA, some viral tRNA and some other components like cyclophilin A which will be used after infection of the next cell.72,73 It appears that the Gag protein mediates the budding process.65 Some of the details involving cellular and viral components have recently been elucidated.74-78 Release is assisted by viral protein Vpu in an incompletely understood process.62

Virion maturation. The immature virion is a not-quite spherical blob with an outer membrane derived from the host cell but including the viral coat proteins gp120 and

12 gp41. The inside has roughly radial alignment of the Gag protein surrounding the RNA79,73 though older work points to a more ordered structure.80 HIV protease (PR) is required at this stage to cleave the Gag and Gag-Pol polyproteins into their constituent structural (p17, p24, p7, p6, p2, p1) and functional (PR, RT, IN) proteins.81,82

External factors. The roles of outside factors has not been outlined here but they certainly should not be discounted. For example, some narcotics have been shown to act at least as cofactors in AIDS.83-85 On the other hand, coinfection with hepatitis G virus (GBV-C) may actually improve chances for survival of AIDS.86-88

1.4 CURRENT TARGETS AND AGENTS OF ANTI-HIV CHEMOTHERAPY

This section will outline some of the chemical agents which are being used or developed to combat HIV. Missing from this list are the most important tools of education and modification of high risk behavior but these social issues are beyond the scope of this thesis.89,7

First contact. The most attractive stage to halt is HIV before it enters the body. Topical microbicides are being sought to prevent transmission. While some previously promising candidates, e.g. the spermicide nonoxynol-9, have suffered doubts regarding toxicity and effectiveness,90 other candidates are being tested.91 A similarly attractive route is to develop a vaccine against HIV. While this wouldn't help those already infected, an effective vaccine could be a safe way to halt the global spread of AIDS. One potential vaccine is currently in phase III trials.92

Virus entry. A CD4 mimic (CD4-IgG2, PRO 542) which binds to gp120 to block HIV attachment is in clinical trials.93,94 AMD-3100, which is currently in clinical trials, is a CXCR4 antagonist which blocks the interaction with the gp120-CD4 complex.95-98 Other compounds are also under development for this target.99-101 In light of the recent discovery of HIV strains which apparently can reproduce in CD8+ T cells in the

13 absence of the CD4 antigen and CXCR4 coreceptor, it is possible that blocking this target may not be completely effective.102

Peptides derived from gp41 have been found to interfere with its ability to initiate cell fusion.103 Pentafuside (T-20, DP-178)104 and T-1249 (DP-107)105 are in clinical trials and even a small, engineered protein is being investigated as an alternative.106

Uncoating the capsid core. Though compounds have been found to interfere with cyclophilin A assisted uncoating,107 there is some doubt that useful drugs can be developed since viral mutants which don't need to incorporate hCyp-18 have been observed.108

Table 1.1. Nucleoside/-tide analog reverse transcriptase inhibitors Name Code Approval a zidovudine AZT/ZDV 1987 didanosine ddI 1991 zalcitabine ddC 1992 stavudine d4T 1994 lamivudine 3TC 1995 abacavir 1592U89 1998 tenofovir b PMPA 2001 emtricitabine FTC phase II/III -- DAPD phase I/II a Year approved for clinical use or current status in approval process. b Prodrug of a nucleotide analog; all other compounds are nucleoside analogs.

Reverse transcription. Since this step is both essential and not duplicated by endogenous enzymes, it has been one of the most active drug discovery targets.109 RT inhibitors such as zidovudine (AZT) were the first clinically approved drugs for the treatment of AIDS. Six nucleoside RT inhibitors (NRTIs) are currently clinically available (Table 1.1): zidovudine,110 didanosine,111 zalcitabine,112 stavudine,113

14 lamivudine114 and abacavir.115 At least two other compounds are currently in clinical trials: emtricitabine116-118 and DAPD.119 These inhibitors function by being integrated during reverse transcription and terminate viral DNA synthesis.120

A nucleotide analog, tenofovir (actually administered as disoproxil fumarate prodrug),121 has been approved recently (Table 1.1). A nucleotide is a nucleoside monophosphate. Since nucleosides must normally be converted to triphosphate form (though some activity has been found for other metabolites of some analogs)122, a nucleotide analog is expected to have the same sort of action as the nucleoside analogs but have more rapid conversion to the active agent.123

Three members of a second class of RT inhibitors, the non-nucleoside RT inhibitors (NNRTIs), have been approved (Table 1.2): nevirapine,124 delavirdine,125 and efavirenz.126 At least four other compounds are currently in clinical trials: emivirine,127-129capravirine,130 calanolide A131 and DPC-083.132 The NNRTIs don't compete with nucleotide binding but interact at an allosteric site to block catalysis.133-

135

Table 1.2. Non-nucleoside analog reverse transcriptase inhibitors Name Code Approval a nevirapine BI-RG-587 1996 delavirdine U-90152 1997 efavirenz DMP-266 1998 emivirine MKC-422 phase III capravirine AG-1549 phase II; on hold calanolide A -- phase I -- DPC-083 phase II/III a Year approved for clinical use or current status in approval process.

15 Integration. Early inhibitors of IN with good in vitro activity failed to elicit sufficient in vivo effect136 but new inhibitors are being developed137-139 and at least one, S-1360, has entered phase II trials.

Production of a new virion. An inhibitor, MPI-49839, which may interfere with the interaction between the p6 tail of Gag and endogenous Tsg101, which is normally involved in membrane sorting, is being investigated in preclinical trials.140,141

Virion maturation. Inhibitors of PR have enjoyed a notable degree of success.142,143 To date, a total of six PR inhibitors (PI) are clinically available (see Table 1.3 and Figure 1.7): indinavir,144 ritonavir,145 saquinavir,146 nelfinavir,147 amprenavir,148 and lopinavir.149 At least four others are currently in clinical trials: atazanavir,150,151 tipranavir,152,153 mozenavir (Figure 4.2),154 and GW-433908.155,156 The current collection of PR inhibitors bind in the active site but an alternative target is the dimer interface (see section 1.6).157,158

Table 1.3. Non-nucleoside analog reverse transcriptase inhibitors Name Code Approval a saquinavir Ro 31-8959 1995 ritonavir ABT-538 1996 indinavir MK-639 1996 nelfinavir AG-1343 1997 amprenavir VX-478 1999 lopinavir ABT-378 2000 tipranavir PNU-140690 phase I/II atazanavir BMS-232632 phase III mozenavir DMP-450 phase I/II GW-433908 b VX-175 phase III a Year approved for clinical use or current status in approval process. b GW-433908 is not a generic name but just an alternate codename for VX-175.

16 N

O O H OH O H O O NNNO S ON N N H H H N CF O O OH 3

Atazanavir Tipranavir

Figure 1.3. Two HIV protease inhibitors in clinical trials.

Alternative agents. A number of natural products have been investigated formally and informally.159-161 Often the mode of action is completely unknown and their effectiveness is certainly questionable but at least one compound, the non-nucleoside RT inhibitor Calanolide A, had reached clinical trials.131,162 The dangerous side of unprescribed drugs has been documented in the interactions between an herbal remedy for depression (St. John's wort)163 and prescribed HIV drugs.164 Problems associated with drug-drug interactions have also been reported.165

1.5 RESULTS OF ANTI-HIV CHEMOTHERAPY

Zidovudine (AZT) was the recommended initial HIV therapy from its approval in 1987 to the mid-1990's. Zidovudine treatment significantly increased the chances for survival for many patients but the effect was limited to no more than two years.166

After a few other NRTIs were approved in the early 1990's, combination therapy of two drugs was found to have a greater effect on the progression of the disease. The drugs zidovudine and lamivudine are synergistic since a common mutation which confers resistance to one does not stop the other drug. While the side-effects of zidovudine can be serious, e.g. anemia through bone-marrow suppression,167 they at least weren't made worse in combination therapy.168

17 Starting with the approval of saquinavir in 1995, PIs have been found to be effective against NRTI-resistant HIV. The PI could act as salvage drugs but their greatest impact was in triple combination therapy: typically two RT and one PR inhibitor. Immune response improved and viral loads decreased dramatically even in patients previously exposed to zidovudine. In the first three years after the introduction of this therapy, the mortality rate dropped from about 30% per year to about 9% in the United States. The effects were so successful that triple combination therapy is now referred to as highly active antiretroviral therapy (HAART).169-172

Considering the clear benefits of HAART, it should be noted that HAART is used for only a small minority of patients; the vast majority of infected individuals receive little or no treatment at all. One factor is high cost, upwards of €10 000/person/year. Deals have been made recently to lower the cost of the drugs by 90-99% to lower-income nations but even this may be too expensive for the poorest. In addition, other issues involving logistics, education and complex social factors must be resolved before HAART can be a treatment for none but the patients in the wealthiest nations.173,174

Side-effects. Considering that AIDS was defined as a disease only about twenty years ago, it shouldn't seem too surprising that the currently available drugs have at least a few imperfections.175 The innately high toxicity of DNA chain terminators (NRTIs) was certainly less significant back in the late 1980's when the alterative was rapid disease progression which almost certainly lead to death. Now that HAART has kept patients alive for years beyond initial estimates, the side-effects of antiretroviral therapy have become both more significant and more apparent.

Probably the most serious side-effect is mitochondrial toxicity associated with NRTIs. Mitochondria are the subcellular organelles which are responsible for generating the energy cells need to function. Disruption of this function in any tissue leads to catastrophic results when the energy demand exceeds the supply. The effect on the liver or pancreas can be fatal. The mechanism is likely to involve inhibition of DNA

18 polymerase γ to disrupt mitochondrial replication but other enzymes may also be disrupted.176,177

Another important side-effect is Fat Redistribution Syndrome or Lipodystrophy. Manifestations of the syndrome include loss/redistribution of body fat, leading to significant changes in appearance, and disturbances to lipid/glucose metabolism, possibly leading to insulin resistance and diabetes.178 It's commonly associated with PI treatment179,180 but NRTIs may play a role (possibly through mitochondrial toxicity) and the condition may arise in treatment-naïve AIDS patients.181 The mechanism of PI associated lipodystrophy is under study.182

Cure? After a few months of HAART, plasma virus levels can drop to almost undetectable levels. Analysis of viral decay rates initially suggested that eradication of the infection might be possible within a few years.183 This estimate hinges on the theory that an insignificant rate of viral replication would neutralize the infected cells and the natural turnover of T cells would eventually purge the body of the infection. Unfortunately, this goal has not been met184,185 as will be described below.

More careful detection techniques have shown that viral reproduction is not completely suppressed under HAART.186-188 Continued viral replication leads to the selection of viable mutant strains to eventually render the current drugs useless.

Reservoirs of latently infected cells are significant to the dynamics of HIV infection. A significant pool of this type is the resting memory CD4+ T cells which form an integral part of long-term immune response by "memorizing" antigens presented in the past. The memory T cells generally remain in a resting state until an appropriate antigen returns but activation can reinitiate infection at least in vitro and presumably in vivo. These cells necessarily have a long half-life (about 44 months under HAART) and it has been estimated that complete turnover could take over 60 years.189

19 Reservoirs of unincorporated virus have been found in various tissues of the body. In this state, the virus is immune to current therapeutic strategies since sometimes the associated cells are not even infected but just have virions adhered to the cell surface. Virus particles in this state have been found to still be capable of reinitiating infection.190-192

Assuming that the enormous financial burden could somehow be overcome, the prospect of 60 years of HAART may actually seem tolerable if one compares this to traditional treatments for a disease like diabetes where injections and monitoring have become a way of life. Since the side-effects of the current generation of HAART drugs are quite serious and may lead to life-threatening conditions, this is not likely to be a viable option. Furthermore, even the less deadly side-effects pose a very serious threat in that it can help dissuade patients from faithfully maintaining the treatment; it can be difficult to endure unpleasant side-effects when asymptomatic. Lapses in therapy can lead to resurgence of disease and resistant strains may be less treatable both for the patient and anyone later infected by this individual.

Future directions. Identification of the specific mutations selected by the current drugs can help identify appropriate drug combinations and define the specificities desired in the next generation of drugs.193,194 One way to get around the resting T cell reservoir is to try to drain them during HAART.195 Early work on this seems promising but not entirely effective.192 It seems reasonable that the reservoirs of unincorporated virus would need to be taken care of too to really show a lasting effect. It can be hoped that a new drug cocktail196 or a vaccine will come along soon to completely cure the disease. For now, the only realistic alternative is continued research into the current and new classes of drugs. Before that "magic cure" is discovered, it is quite possible that future therapies will include an assortment of drugs against HIV, drugs to bolster our natural response and drugs to counteract the side- effects of the other drugs.

20 1.6 HIV PROTEASE

The HIV protease (PR) was postulated to belong to the family of aspartic acid proteases based on the identification of the Asp-(Ser/Thr)-Gly catalytic triad.202 Other members of this family, including the endogenous enzymes Pepsin, Cathepsin D and Renin, are single chain proteins of over 300 residues folded into two domains; each of which supplies a catalytic triad of Asp-(Ser/Thr)-Gly. PR is much smaller at only 99 residues in length and possesses only a single Asp-Thr-Gly triad so a homodimeric structure was proposed.203 Both of these conjectures were later confirmed by X-ray crystallographic analysis of the apoenzyme204,200 and of a PR-inhibitor complex.205,206 a) b)

Figure 1.4. Ribbon drawings of (a) apo- and (b) inhibited HIV protease showing the relatively open and closed position of the flaps (top of the images). These images were produced with MolScript197 and Raster3D198 on the PDB199 files 3HVP200 and 1AJX.201

The X-ray analyses revealed that the PR has C2 symmetry around a central active site. The dimers are held together predominantly through interdigitated beta-sheets formed at the base of the enzyme by the N- and C-termini of each monomer. The catalytic cavity is covered by highly flexible flaps. In the apoenzyme (Figure 1.4a), these flaps are in open position but when an inhibitor (or substrate) is bound (Figure 1.4b), the flaps close down. The exact C2 symmetry of the enzyme can be broken upon the binding of an inhibitor but the overall shape remains relatively consistant regardless of the nature of the ligand (with the exception of very large molecules like fullerene derivatives207).208

21 The active site is a channel which has subsites for eight consecutive residues which in the usual nomenclature210 are designated S4 to S1 before and S1' to S4' after the scissile bond. The R-groups of the amino acids, or equivalent structures in non- peptidic inhibitors, are designated P4-P4' to correspond to the appropriate subsites (Figure 1.5).

P2 P1' P3'

O H OH O H N N NN NN N N H O OH H O

P3 P1 P2'

209 Figure 1.5. A C2-symmetric HIV protease inhibitor (A-76928). P3-P1 and P1'-P3' represent the side chains intended to interact with the S3-S1 and S1'-S3' subsites, respectively.

The PR cleaves a variety of peptide bonds in the viral polyproteins during the course of its action to produce the individual proteins of the mature virus. The active site constellation of two proximal carboxyl groups from the Asp25/Asp25' residues (one from each monomer) and a water molecule coordinated between the two carboxyl groups are essential for catalytic activity. A hypothesis of the mechanism for peptide bond cleavage by the PR is shown in Figure 1.6211,212 Recent studies213,214 have challenged some of the details but the classical mechanism has been the starting point for the design of many inhibitors incorporating transition state (TS) mimics based on the putative tetrahedral intermediate shown in Figure 1.6 formed by the hydration of the amide carbonyl group.215 All six of the currently approved PR inhibitors are hydroxyethylene TS analogs, i.e., where the scissile amide bond is replaced by –

CH(OH)CH2–.

22 Tetrahedral Intermediate R1 H N R1 H N R N H 2 N O R H O OH 2 H O H O H O O O

OH O - H O O δ δ- Asp25 Asp25' Asp25 Asp25'

R 1 H R1 N N OH H2N R2 N H O OH R2 H O H O O O O

O - H O OH O δ δ- Asp25 Asp25' Asp25 Asp25' Figure 1.6. Schematic representation of the catalytic mechanism of aspartic acid proteases.

O H O N H NNOH H OH N N N N N N H O OH O O HN O H2N

Saquinavir Indinavir

O H O NH2 N O O SNN N O O N S N H OOHH S ON N O H OH

Ritonavir Amprenavir

OH O H H S O N O N NNH O N HO O H O N N H OH

Lopinavir Nelfinavir Figure 1.7. Clinically approved HIV protease inhibitors.

23 2 COMPUTATIONAL CHEMISTRY

Computational Chemistry: A discipline using mathematical methods for the calculation of molecular properties or for the simulation of molecular behavior....216

Calculations for computational chemistry may be performed with anything from massively-parallel super computers, desktop workstations, standard PC's or just a pencil. Like so many other sciences, the dramatic increases in readily available computational power has made some calculations considered too daunting to seriously consider even 30 years ago seem almost routine today. For example, a bench chemist who hits the "clean-up" button in a chemical sketching program would probably not consider himself to be performing "computational chemistry": it's just too easy.

Working from this general definition, the field of computational is quite broad and varied. Techniques of computational chemistry used in this study have included: quantum mechanics, molecular mechanics, quantitative structure-activity relationship (QSAR) analysis and experimental design. While these are in effect just tools, an understanding of their working is hoped to provide a context for the chemically and biologically relevant aspects of the present study.

2.1 QUANTUM MECHANICS

The familiar principles of Newtonian mechanics work amazingly well for most anything directly observable. But when one inspects a system in minute detail, e.g., at the atomic level, the simple equations aren't quite enough. Quantum mechanics (QM) is a theory which states that there are discrete (quantized) levels of energy for a system. The energy levels are so close together that the smooth functions of Newtonian mechanics can be seen as an approximation of QM at high energies.

24 The basic equation QM is the deceptively simple HΨ = EΨ, where H is the Hamiltonian operator, Ψ is the wave function, and E is the energy. Actually, this is just the short-hand form for the time-independent, non-relativistic Schrödinger equation but the mathematical details are not really necessary here. In theory, the Schrödinger equation should be able to describe nearly everything in chemistry (one needs to add the relativistic mechanics of the Dirac equation to cover all of chemistry).

Unfortunately, exact solutions to the Schrödinger equation have only been found for fairly trivial systems but the application of some approximations, chemically interesting systems can be treated. One of the complications of the Schrödinger equation is that the motion of the electrons and nuclear particles are coupled. Given that the mass of a nucleus is thousands of times greater than that of an electron, their relative motion can be approximately regarded as independent. This is called the Born- Oppenheimer approximation and its application allows the electronic and nuclear components of the Schrödinger equation to be solved separately. The electronic component takes the greatest attention in QM but the nuclear component describes the nuclear motions of spectroscopy as well as geometry optimizations. In a way, molecular mechanics (Section 2.2) can be considered to work on the nuclear component of the Born-Oppenheimer approximation.217,218

There are many other approximations which may be alternatively applied but from a practical point of view, e.g., when using standard QM software, the two most important issues to consider are which "level of theory" and which "basis set" to use.

Level of theory. With the application of a few principles of physics, like the Pauli exclusion principle, QM in the context of chemistry (quantum chemistry) can be divided to ab initio and semiempirical calculations of molecular systems. These methods and their subtypes are often discussed as differing levels of theory where the Schrödinger equation would be considered the generally inaccessible pinnacle.

25 Ab initio roughly translates in this context as "from first principles" to denote that the calculations are performed without experimental parameters. The basic, modern implementation of ab initio is Hartree-Fock (HF).219 It basically extends a Born- Oppenheimer type approximation to separately consider each wave function (Hartree's theory) but tries to account for average field of electron repulsion (Fock's integrals). Other commonly used methods, MP2 (second order Møller-Plesset perturbation theory), MP3, etc treat electron correlation more accurately.220 This generally produces better results but does so at a fairly high computational cost.221

An alternative approach to the MPn methods is Density Functional Theory (DFT).222,223 While not strictly an ab initio method, since it includes a few empirically derived parameters, it can achieve quite accurate results with only a modest increase of computation time.221,224

Even with the use of limited basis sets and a moderate level of theory, ab initio calculations can be quite computationally demanding. While this is certainly much less of a problem today than even a few years ago, semiempirical methods225 greatly expand the class of problems which can be studied. Semiempirical calculations achieve their speedup by using a series of parameters to approximate the results of ab initio calculations. Semiempirical calculations are frequently used today to calculate approximate atom charges or to quick determine reasonably accurate geometries and energies for many systems. They are also frequently used by QM software to "jump start" ab initio calculations by calculating reasonable wavefunctions.

Basis sets. The ab initio methods can in principle be used to solve hydrogen atom orbitals and then apply these solutions with their approximations to treat realistic molecular systems. In practice, the orbitals are replaced with a series of approximation functions, typically gaussians, which are collectively referred to as basis sets. Using a large number of functions better approximates the real orbitals but (as expected) increase the computational cost. Several standard basis sets are common usage, e.g. STO-3G, 3-21G and 6-31G, but many other sets are available. Other choices are

26 whether to add polarization (e.g., 6-31G* to add d orbitals) and/or diffuse functions (e.g., 6-31+G* to also allow the orbitals to expand).221

2.2 MOLECULAR MECHANICS

A great simplification in molecular calculations is to simply ignore the motion of the electrons and go back essentially to Newtonian mechanics. This is molecular mechanics (MM) where an empirical force field (FF) describes molecular structure in terms of average bond lengths, angles, torsions, etc and energetics in terms of force constants. The force constants are restraining potentials generally approximated with simple harmonic functions but sometimes with higher order terms. The FF is parameterized to approximately reproduce various experimental results from spectroscopy, calorimetry and possibly QM.

The chief advantage of MM is the incredible reduction in computational requirements: both in computation time, on the order of several orders of magnitude, and memory. This allows MM to be applied to systems which are impractical for QM.

The main limitation with MM is its dependence on the parameterization for accuracy. For example, to properly simulate bond stretching, a good FF should: (i) provide reasonable forces and distances for every combination of atom pairs within its intended area of application (e.g., C–C, O–H, P–O, etc for biochemistry); (ii) account for bond order (e.g., C–C versus C=C); (iii) consider immediate chemical environment (e.g., a nitrogen of an amine doesn't act the same as a nitrogen of an amide); and (iv) even considering effects of neighboring atoms (e.g., a carbons attached to the amide or amine nitrogens will act differently). Add to this the combinations for three atoms (bond angles) and then for four atoms (torsions) and the complication becomes clear. A compromise must be made between accuracy and applicability (the transferability of the parameters for one system to another). Many different FFs which have been parameterized in different ways are currently in use. The different FF implementations have somewhat different areas of applicability.226

27 Procedures combining MM with QM have appeared. The best features of both can be combined by treating some critical portion of the chemical system with QM and the remainder with MM. This can be especially useful to simulate a chemical reaction since bond breaking can't generally be simulated in MM; hard to follow the electrons when none are present in the model.227,228

2.3 QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR)

A basic premise at the core of medicinal chemistry is that similar structures can be expected to exhibit similar biological activity. Formalization of this hypothesis into "structure-activity relationship" (SAR) studies can help a chemist to design new compounds with improved activity by analyzing the effects of substituents about a common core. While counter examples can be found, this model is a basic tool of the science.

A simple extension to SAR is to move from the qualitative to the quantitative (QSAR) with the derivation of a mathematical formula to relate some quantifiable properties to activity. These properties, often referred to as chemical descriptors, can be experimentally derived or calculated quantities. Typically, QSAR studies focus on the effects of a few substituents of a narrow congeneric series but the technique can be generalized to use whole molecule properties like logP or polarizability.

Measurements of molecular properties can include descriptors based on their relative three-dimensional properties. This technique, termed 3D-QSAR, has the potential to be more interpretable since the descriptors can be more closely related to what a real receptor feels. The greatest advantage of 3D techniques is their ability to move away from congeneric series. Basically any combination of skeletons can be combined as long as the molecules share the same mode of action at the receptor.

CoMFA. One popular application of the 3D-QSAR method is CoMFA (comparative molecular field analysis).229 CoMFA works by calculating interaction energies of a

28 probe atom with each compound of the dataset. The correlation between these interaction energies and the measured biological activities is used to derive an image of which regions in space are beneficial or deleterious to the activity.

To be directly comparable, the ligands must be aligned and oriented in their putative bioactive conformations. Determination of this alignment rule is often considered the most challenging aspect of CoMFA. Mutual alignment might be aided by identification of a common pharmacophore using the active analog approach230 or some other information. In fortunate circumstances, a ligand-receptor crystal structure is available for guidance.

When all compounds in the data set have been superimposed they are located in a grid box and the interaction energies (typically limited to the steric and/or electronic interactions) between a selected probe atom and each molecule individually are calculated at every lattice point in the grid box (Figure 2.1). This could be thought of as painting a crude image of a receptor based on the 3D properties of the ligands.

Figure 2.1. The interaction energies between the probe atom and all molecules are measured at each grid point on a regular 3D-grid. Each point in space becomes a descriptor variable in a QSAR analysis.

The steric (Lennard-Jones) and/or electrostatic (Coulombic) field energies thus calculated become descriptors in the CoMFA table. The QSAR is generated by a PLS

29 (partial least squares) analysis of the data contained in the table. PLS is a technique to extract the principle components from a potentially large number of columns (the interaction energies in CoMFA) in such a way to maximize the correlation with some other value(s), e.g., biological activity. The statistical quality of the model can be determined through the value of the crossvalidated r2 (q2). In crossvalidation, the predictive ability of the model is estimated by repeatedly running PLS while leaving out one (or more) compound(s) at a time until each compound is excluded. In each round, the activity of the compounds that were left out is predicted. The q2 is computed as a summary of the crossvalidation rounds and accumulates for each component of the PLS. The number of components to use can be guided by the internal statistics of the method which report a standard error of prediction. A model with a q2 below about 0.3 is probably unacceptable since that value could be found by chance correlation (the exact cutoff value is a function of the number of compounds).231 A final CoMFA model is then derived (without crossvalidation) using the optimal number of components determined above to give a correlation coefficient (r2) for the model.

30 3 AIMS OF THE PRESENT STUDY

This investigation is part of a research project aimed at the development of novel and specific HIV-1 protease inhibitors. The specific objectives of this study have been:

(i) To elucidate the binding mode preferences of a cyclic sulfamide-based series of HIV-1 protease inhibitors.

(ii) To derive useful structure-activity models of the cyclic sulfamide-based inhibitors to aid future synthetic efforts.

(iii) To derive similar models based on the individual components of affinity, namely association and dissociation rates, for a more diverse set of HIV-1 protease inhibitors.

During the course of these studies, a new objective was identified:

(iv) To derive an accurate set of empirical FF parameters for sulfamide derivatives in order to facilitate more accurate molecular modelling.

31 4 CYCLIC SULFAMIDE-BASED HIV PROTEASE INHIBITORS

This chapter includes a background and summary of computational details of Papers I and II of the complete thesis.232,233

Figure 4.1. Structural water-301 hydrogen bonding to the backbone NH's of Ile50 and Ile50' and the carbonyl oxygens of a linear inhibitor (BEA-322).234

4.1 CYCLIC UREA-BASED INHIBITORS

X-ray analysis of complexes of HIV PR with linear PIs generally include a tightly bound, structural water molecule (Wat-301) bridging the enzyme flaps to the inhibitor through hydrogen bonds to the Ile50/Ile50' amide hydrogens and the P1/P1' carbonyl oxygens of the inhibitor (Figure 4.1). Hoping to reap entropic gains, researchers from DuPont-Merck Pharmaceuticals looked for a way to incorporate that conserved water into an inhibitor. Lead compounds were identified in a 3D database search and developed into a distinctly non-peptidic series of cyclic urea-based inhibitors including DMP-323 (Figure 4.2a).235 DMP-323 entered clinical trials but was later withdrawn due to undependable bioavailability associated with its poor water solubility. Another

32 member of this series, DMP-450, also known as Mozenavir (Figure 4.2b), has substantially improved solubility and is currently in clinical trials.154,236

a)O b) O

HON N OH N N H2N NH2

HO OH HO OH

DMP 323 DMP 450

Figure 4.2. Cyclic urea-based HIV protease inhibitors (a) formerly or (b) currently in clinical trials.

Figure 4.3. HIV protease inhibitors AHA-001 (black) and A-76928209 (grey) taken from the X-ray coordinates of protease-inhibitor complexes 1AJX and 1HVK, respectively. Alignment was performed by superimposition of the backbone atoms from each complex.

Our laboratory had also started to explore Wat-301 mimics237 but following the publication of DMP-323, interest was drawn towards the design and synthesis of cyclic urea derivatives.238,239 The parent compound of this series, AHA-001 (Figure 4.4a), has been co-crystallized with PR and the coordinates are available from the PDB as entry 1AJX.201 The synthesis of AHA-001 and its derivatives is based on mannitol (though other diastereomers have also been synthesized). The sugar sets the four chiral

33 centers and the P1/P1' phenoxymethylenes extend a bit beyond the benzyls of DMP- 323. The overall binding mode of AHA-001 mimics that of DMP-323 which in turn mimics the binding mode of many peptide-based linear inhibitors. Figure 4.3 shows the similarity in binding of AHA-001 to a C2 symmetric linear inhibitor, A-76928 (Figure 1.5).209 Cyclic urea AHA-001 overlaps the P2, P1, P1', P2' and vicinal diol (transition-state mimic) of A-76928. Also shown is the proximity of the carbonyl oxygen of AHA-001 to the conserved structural water (Wat-301) associated with A- 76928.

4.2 CYCLIC SULFAMIDE-BASED INHIBITORS

A number of other mimics for water-301 have been incorporated into inhibitors designed and synthesized by other research groups including phosphordiamidate,240 sulfoxide,241 sulfone,242 sulfamide,243 guanidine,244 oxamide245 and azalactam.246 Much of the work of the present study has focused on the cyclic sulfamide-based

238 inhibitors. A brief note on nomenclature: sulfamide derivatives (R2NSO2NR2) are related to ureas (R2NCONR2) just as sulfonamides (R2NSO2R) are related to amides

(R2NCOR).

(a) (b) S2 S2´ S2

S1´ O OO S N N N N O O O O S1 S1´ S1 HO OH HO OH S2´ AHA001 AHA006 Figure 4.4. Cyclic urea- and sulfamide based HIV protease inhibitors from our laboratories. The enzyme's subsites are marked to indicate, according to X-ray analysis, where the side-chains have been directed.

34 The parent compound of this series, AHA-006 (Figure 4.4b), is chemically identical to AHA-001 except for the replacement of the water mimic. We expected that this compound would adopt a binding mode similar to that of AHA-001. Preliminary X-ray results based on this assumption showed strong distortions in the P1'/P2' arms of AHA-006. Since it looked like the apparent (by analogy to AHA-001) P2' was too long and the P1' was too short, maybe those groups have somehow switched positions. Molecular mechanics calculations on the preliminary X-ray coordinates using MacroModel 4.5247 were set up to test this hypothesis. The P1', P2' and the their attachments in the central ring were allowed to relax while remaining ring atoms of the central ring, P1 and P2 were constrained by a strong potential (100 kcal/mol). A short calculation in vacuo under the AMBER* FF248 supported the hypothesis by quickly switching the positions of what had been assumed to be the P1' and P2' (Figure 4.4).

Reanalysis and refinement of the X-ray data down to a 2.0 Å resolution showed a decidedly nonsymmetric twist in the central ring associated with a switch of the P1'/P2' side chains relative to AHA-001.201 The superposition of AHA-001 and AHA-006 from their protease complexes is shown in Figure 4.5. The phenoxymethylene groups of each are colored black to illustrate the differences in binding conformation. The inhibitors show pretty good overlap on the P1/P2 side where both phenoxymethylene fit into S1. On the prime side, Figure 4.5 shows that the phenoxymethylene of AHA- 006 is placed into the S2' pocket and reaches further in than the benzyl of AHA-001.

4.3 STUDY OF THE RING FLIP

The X-ray analysis of one complex, even at good resolution (2.0 Å), may not be enough evidence to be certain that the same binding mode would be adopted by the whole series of compounds. We broke down the problem into two related sub problems: (i) Is the flip induced by enzyme binding or is it a favored conformation in bulk solution? In other words, is the twisted ring conformation the cause or result of switched P1'/P2' groups? (ii) Regardless of the cause of the flip, could it be controlled? Could we force a symmetric binding mode similar to the one seen in the urea

35 derivatives by appropriate substitutions in the sidechains or, conversely, can we depend on the flip to be there when we model new compounds? The latter question will be addressed in Section 4.4.

Figure 4.5. HIV protease inhibitors AHA-001 and AHA-006 taken from the X- ray coordinates of protease-inhibitor complexes 1AJX and 1AJV, respectively. Alignment was performed by superimposition of the backbone atoms from each complex.

The Cambridge Structure Database (CSD)249 was searched for sulfamides to address the first question. The closest match was a seven-membered cyclic sulfamide ring with a fused benzene ring in place of the diol (Figure 6.1; CSD code: SIKFUN).250 The fused benzene ring certainly has an effect on the geometry but the sulfamide portion of the ring matched qualitatively well with AHA-006 to lend some small bit of evidence in favor of the idea that AHA-006 could form its geometry in solution (Figure 4.6a).

We have also tried to address the first question with molecular modeling by calculating the energy difference between the nonsymmetrical and symmetrical conformations. For the sake of simplicity, the twisted and symmetrical conformations of AHA-006 were modelled with the side chains truncated to methyls (Figure 4.6b). The starting geometry for the twisted conformation was taken from X-ray. The symmetrical conformation was modelled with MacroModel 5.5 using AHA-001 as a template. Restraining potentials on the side chains and diol were used and gradually

36 diminished to guide the geometry to a stable point. Both model compounds were relaxed to their nearest local minima. a) b)

Figure 4.6. (a) Superimposition of the sulfamide ring of SIKFUN250 (black) and AHA-006 (grey). In this drawing of SIKFUN, the fused benzene opposite the sulfamide was omitted and ethyl acetate in position 3 was truncated to methyl. (b) Superimposition of the modelled symmetric (black) and observed nonsymmetric (grey) conformations of the central ring of AHA-006.

In MacroModel 5.5, two FFs, AMBER* and MMFF94,251 were considered good candidates since they could be used for the inhibitor in vacuo and latter to model the protein-inhibitor complex. AMBER* reported low quality parameters for bond stretch, angle and torsion terms involving the sulfur (actually, S-N-C-C torsions were reported to be high-quality parameters even though the force constants were all zero). Attempts to augment the parameters for sulfamide will be presented in Chapter 6. MMFF faired better by reporting at least medium quality stretch and angle terms but many torsions involving the sulfamide moiety (and elsewhere) were of low quality. Minimization (without conformational analysis) from the X-ray coordinates by MMFF produced no gross changes to the geometry. Considering this, MMFF was used for the general modelling but molecular mechanics was judged to be unsuitable for the comparative energy calculations.

37 Semiempirical methods were considered briefly for the energy calculations but doubts regarding parameterization252 prompted us to turn to ab initio calculations. The choice of an appropriate basis set is generally important since it can strongly effect the accuracy of the results or, just as easily, consume undue resources (see Section 2.1 for a review). Mó et al253 have suggested that 3-21G* is minimally required for sulfonamide but they and others (calculating on sulfonamide)254 have generally used the 6-31G* basis set with the conclusion that larger basis sets added little accuracy (relative to their experimentally derived values). The other consideration is what level of theory to use. These researchers used Hartree-Fock (HF) but correlation effects with levels of Møller-Plesset perturbation theory (MP)220 have also been explored for sulfonamide and sulfamide.254,252 The opinions were mixed on the necessity of MP considering their great computational cost so a compromise was chosen: density functional theory (DFT). DFT calculations run at about the speed of conventional HF but account for some electron correlation effects like MP.221 The B3LYP hybrid functional255,256 was chosen as the specific implementation of DFT based on it's generally good reputation.221

Geometry optimization using B3LYP/6-31G* was performed using Gaussian94257 to find that the nonsymmetric conformation was favored for the model compound by 10 kJ/mol (2.4 kcal/mol). These energetic calculations support the interpretation of the X- ray data and imply that the flipped conformation is achievable outside the protease active site (with the caveat that only two conformations were studied). The 10 kJ/mol energy difference also gives a hint for the second problem: the energy difference may be surmountable within the enzyme with proper substitution of AHA-006.

4.4 GENERALITY OF THE RING FLIP

Well satisfied that the observed flip was at least reasonable, the second question of this section of the study remains: Could we design compounds which would adopt a symmetric binding mode or is the flip dependable? Six derivatives of AHA-006 (Figure 4.7) were designed to have good to strong preferences for the S2/S2'

38 subsites258 which would be satisfied only if they would adopt a symmetrical, "urea- like" conformation. A modelling study of these compounds was instituted to address this problem.

OO OO OO OO S S N N N N S O O HO OH O N N O

OO OO O OOO HO OH HO OH HO OH

AHA019 AHA021 AHA022

O O OO OO HO OO OH S S N N N N N N S OH OH N N

OO OO OO HO OH HO OH HO OH

AHA023 AHA025 AHA030

Figure 4.7. Compounds synthesized to test sulfamide ring flip hypothesis.232

Ambiguity regarding the Z/E configuration of the ketoximes of AHA-030 hindered its modelling. Since it was predicted to be the compound most likely to force symmetrical binding, its accurate modelling was important. A search of the CSD for acetophenone oximes revealed 12 entries for E and 1 entry for Z. Energy calculations were carried out to test the hypothesis of a more stable E isomer. An in vacuo conformational analysis centered on the ketoximes and associated phenyl rings of AHA-030 was performed with the MMFF force field of MacroModel 5.5. The E isomer was calculated to be 14 kJ/mol (3.4 kcal/mol) more stable than the Z isomer. Since oximes are not well parameterized in the version of MMFF used, a quantum mechanics calculation was performed. The E and Z isomers from the MMFF calculation were optimized at B3LYP/6-31G*. The E isomer was found to be 11 kJ/mol (2.6 kcal/mol) more stable. With the assumption that the synthetic reaction used wasn't overwhelmingly kinetically controlled in the Z direction, we used the E isomer in our models.

With a working model for the AHA-030 ketoxime in hand, we then proceeded with the modelling of the AHA-006 derivatives in both the symmetric and conformations

39 nonsymmetric. Starting structures for each were taken from the model compounds discussed in Section 4.3. Since the benzyl derivatives attached to the ring at the nitrogens (i.e., the P2/P2' groups in a symmetrical conformation like AHA-001) were the only portions which differed from AHA-006, we made the simplification in the modelling work that the remaining portions of the inhibitors remained fixed. While this assumption was far from rigorous, it did avoid the problem of inadequate parameters in the empirical FFs mentioned in Section 4.3. Minimization and conformational analysis centered on the benzyls and their attachments was performed with the AMBER* FF and a GB/SA solvent model259 within the active site of the protease from the AHA-006 complex.

The modelling predicted that the symmetric conformations of all derivatives would allow favorable contacts with either Asp29/Asp29', Asp30/Asp30' or reach into the solvent. Nonsymmetrical conformations would necessarily loose the S2' pocket's polar interactions but they may be able to favorably interact with Arg8.

The derivatives were synthesized and tested against HIV-1 PR. By comparison to the

Ki values of similar cyclic urea derivatives made elsewhere, we concluded that the most reasonable SAR would support the hypothesis that all of these derivatives adopted the nonsymmetrical conformation observed for AHA-006. The X-ray structure of AHA-030 in PR was determined and the nonsymmetric binding mode was clearly visible. With this last bit of confirmation, we've concluded that the nonsymmetric binding mode seems to be reproducible and robust.

4.5 EXPLOITATION OF THE RING FLIP

Convinced of the dependability of the nonsymmetric conformation, we decided to make a series of chemically (rather than merely conformationally) nonsymmetric derivatives which we hoped would be better adapted to the binding sites. While the S1' and S2' pockets share similar characteristics, they are certainly not equivalent.260 A good P1' group cannot be expected to be optimized for the S2'. This is illustrated in

40 Figure 4.5 with the parent compounds AHA-001 and AHA-006: note how much further AHA-006 reaches into the S2'. These differences in SAR should be understood and exploited to improve the binding of the cyclic sulfamide inhibitors.

OO O O O OO OO S N N N HON N O S S H N N H N N H

OOO HO OH OO OO HO OH HO OH

AHA024 AHA047 AHA045

Figure 4.8. Two of the chemically nonsymmetric AHA-006 derivatives (AHA- 024 and AHA-047) synthesized to take advantage of the expected nonsymmetric binding mode and one of the symmetrical derivatives (AHA-045) for comparison.233

Eleven chemically nonsymmetric derivatives were synthesized along with seven new, symmetric derivatives to help with the interpretation of the SAR (a few representative structures appear in Figure 4.8). With the advantages we hoped to gain with this chemical asymmetry, we received the complication that we can't a priori determine which side of the inhibitors will adopt the flip: left or right. Because of this, conventional SAR was not readily interpretable. We decided to use a quantitative method to model which flip had been adopted by each nonsymmetric compound. The decidedly three-dimensional nature of the problem prompted us to consider 3D-QSAR. Many other QSAR261 and 3D-QSAR studies262-269,233 have been made on HIV-1 protease but this study was intended primarily to model the choice of ring flip.

As noted in Section 2.4, the relative alignment of the compounds is of critical importance in 3D-QSAR. Fortunately, the crystal structure of AHA-024 complexed with HIV-1 PR was solved to 1.8 Å resolution and available (PDB code: 1G35) to aid in the alignment of the nonsymmetric compounds. Incidentally, the X-ray structure showed a twisted ring conformation and the flipped P1'/P2' side chains in agreement with AHA-006 and AHA-030.

41 One flip conformation was known but the problem of the others remained. Conformation-independent techniques have appeared in the literature270,271 but considering the availability of good crystal structures to guide the fit of our congeneric series, we wanted to take full advantage of the information we had. Another variation is 4D-QSAR which can consider many conformations simultaneously.272 Since we only wanted to consider two conformations per nonsymmetric compound, the extra complications involved in 4D-QSAR (i.e., genetic algorithms) seemed unnecessary. In the end, we opted to use standard CoMFA229 to generate models for all possible flip combinations of the nonsymmetric compounds. With eleven nonsymmetric compounds (considering AHA-024 as a control) in two different binding modes we need 211, or 2048, CoMFA models. Exploration of the most suitable set of CoMFA parameters would not be easy to achieve for this many models so we settled for the very limited survey of only the ten fields offered in the Advanced CoMFA package. In total, we now had 20480 models to generate.

Running 20480 CoMFA calculations interactively would at the very least be terribly boring so these calculations were run in batch mode using a script. The details of the procedure, along with a more complex CoMFA analysis, will be presented in Section 5.6.

To prepare the dataset for CoMFA, minimization and conformational analysis using AHA-006 and AHA-024 as templates was set up and performed basically as described in Section 4.3, with the exception that only the nonsymmetric conformations were being considered. The eleven nonsymmetric compounds were modelled in both flip conformations. AHA-006 and the six derivatives from the previous study were also included in the models to bring the total to 25 compounds in each model.

The q2 (crossvalidated correlation coefficients) values from the CoMFA calculations were used as a rough measure of the quality of the alignment (binding mode) of the nonsymmetric inhibitors. The numerically sorted q2 values from these models (Figure 4.9) form a normal distribution curve with a few values peaking above 0.7. The top 20

42 models were considered carefully in the context of molecular modelling. The model with second highest q2 was eventually chosen though several other models were just about as reasonable. This ambiguity could be rationalized in several ways but the most significant may be that some of these inhibitors may be able to bind almost equally well in either flip conformation. The CoMFA calculations could conceivably be modelling this accurately but this may be pushing the data a bit too far.

Figure 4.9. Crossvalidated correlation coefficients (q2) for 20480 CoMFA models (sorted by q2).

Before publication of these results, the crystal structure of AHA-047 complexed with HIV-1 PR was solved to 1.95 Å resolution (PDB code: 1G2K). The X-ray structure again showed a twisted ring conformation and the flipped P1'/P2' sidechains in agreement with the other sulfamides. The twist was seen to lie on the side of the unsubstituted benzyl in agreement with the conformation in the chosen CoMFA model.

As stated, the purpose of the CoMFA calculations was really just try to figure out the binding modes; to help make a reasonable guess as to which flip each nonsymmetric inhibitor might adopt. But with a guess of the alignment, we started a more conventional CoMFA calculation on the dataset. For this calculation, we used the 18

43 compounds of the current study233 as a training set and the seven old compounds (the six from the previous study232 plus AHA-006) as a test set. The resulting model (q2 = 0.54, r2 = 0.96, 3 components) predicted the test set reasonable well with a mean absolute residual pKi of 0.58.

44 5 KINETIC ANALYSIS OF HIV PROTEASE INHIBITORS

This chapter includes a background and summary of computational details of Papers III and IV of the complete thesis.273,274

In the early phases of drug development, an understanding of the details of receptor interaction and of the ADME (absorption, distribution, metabolism and excretion) profile of the candidates can be of great importance.275,276 But regardless of how well the system is understood, some information regarding the affinity for the target must be ascertained. In this age of high-throughput screening,277 this information might come in the form of a binary, yes/no answer instead of a precise value possible with

"low throughput" Ki determinations.

An intermediate method which can be used to complement these two extremes are the aptly named moderate-throughput screening methods. The instrument which has been used in this study is a surface plasmon resonance (SPR) based biosensor. One of the advantages of using this sort of instrument is that the affinity measurement, KD, is broken up into its constituents of association rate (kon) and dissociation rate (koff). The significance of having access to this extra information will be discussed in Section 5.4.

5.1 THE TECHNOLOGY OF SURFACE PLASMON RESONANCE BIOSENSORS

When polarized light is shown through glass onto a thin metal film, there is a dip in the intensity of the reflected beam at a specific angle of incidence. This angle is sensitive to the refractive index at and near the surface.278 When molecules bind to that surface, the refractive index changes and this, in turn, changes the angle for maximum absorbance. Detecting this change of angle over time is at the heart of an SPR sensor. When biologically interesting molecules are immobilized onto this surface, e.g. a protein or antibody, it becomes a biosensor which can detect molecular binding in real- time without the need for fluorescent or radioisotopic labels.279-281

45 The generalized schematic of a flow cell in Figure 5.1 illustrates the essential components of an SPR biosensor. Several of these flow cells may be within an instrument to allow the simultaneous detection of the same substance binding to several different immobilized targets. Subtraction of the signals across the detectors, e.g. one for specific binding to a receptor and another for non-specific binding to albumin, could reduce the errors associated with subtracting the final values from separate experiments. Alternatively, different substances, e.g. reference and test compounds, could be run in each channel against the same type of target to achieve similar benefits.

Figure 5.1. Schematic diagram of a surface plasmon resonance biosensor.

The SPR signal is expressed as resonance units (RU) and the continuous display of RU as a function of time is referred to as a sensorgram. The idealized sensorgram shown in Figure 5.2a illustrates the basic stages of the instrument's cycle: (i) buffer blank pulse used as a negative control and to detect carryover between samples; (ii) sample pulse separated into association (during sample injection) and dissociation (after sample injection) phases and used for identification of binders; (iii) regeneration of the sensor surface used to remove slowly dissociating binders; and (iv) system wash to rinse the autosampler and the injection unit. Injection of a sample that interacts with the sensor surface results in a signal that, after subtraction of the reference signal, is proportional to the amount of bound ligand. The dissociation phase starts at point D, once the

46 injection has been switched from sample to running buffer. The rate of signal increase during sample injection (starting at A) provides the observed association rate constant

(kon), while the observed dissociation rate constant (koff) is obtained from the rate of signal decrease after point D. a) b) Regeneration Sample Wash Blank 300 A 200

100

Response (RU) 0 D -100 0 300 600

Time (s)

Figure 5.2. (a) Cartoon of response (RU) versus time for a cycle of an SPR biosensor showing the stages of blank injection, sample injection, regeneration and wash. (b) Typical sensorgrams (truncated on the y-axis) for three HIV-1 protease inhibitors. Reporter points for the association phase (A1 and A2) and dissociation phase (D1 and D2) are indicated.

5.2 AN SPR SCREEN OF HIV PROTEASE INHIBITORS

The development of biosensor technology has provided a new tool for rapid kinetic studies of biomolecular interactions and recent improvements in sensitivity and methodology allows the technique to be used for interaction studies with low molecular compounds as analytes.281-283 The work in our laboratories has focused on the interaction between HIV-1 protease and inhibitors.284-287

47 The present study describes a screen of 290 HIV-1 protease inhibitors.273 These structurally diverse compounds included both linear and cyclic inhibitors culled from several cooperating laboratories. The structures covered a range of molecular weights from 232 to 1093, MlogP (calculated Moriguchi logP) from -2.7 to 5.2 and possessed from 3 to 20 hydrogen bond acceptors. Reliable inhibition data (Ki) was available for all for comparison purposes. These values ranged from 70 pM to a cutoff of 10 µM for inactive compounds.

Figure 5.3. Association phase reporter point A1 versus measured pKi (-log10 Ki). The group of points with a pKi of 5 represent a cutoff of 10µM for Ki imposed on the data.

A typical sensorgram from three HIV PR inhibitors is shown in Figure 5.2b. The reporter points for the association phase (A1 and A2) and dissociation phase (D1 and D2) served as the primary data in the study where each compound was run at a single concentration. Using one report point of the association phase (either A1 or A2), we 2 found a reasonably good correlation (r = 0.72) to pKi (Figure 5.3). It should be noted that this correlation overcomes the fact that the buffer for SPR284 used only 0.15 M

239 NaCl which is quite different than the 1 M NaCl typically used for the Ki determinations.288

48 5.3 ANALYSIS OF THE SCREENING DATA

The results presented in Section 5.2 relied on processing the raw data from the sensorgrams with the instrumental software. At that point, there was still a large amount of data to consider for a screen of 290 compounds. Besides the four reporter points, there was data for the washing and regeneration stages as well as correlations to the results from the other channels besides HIV PR.

The processing of this data was aided by scripts written in the Perl programming language289 to filter out dubious values and find reasonable thresholds for some of the variables to achieve the high correlation of biosensor data with previously measured activities. An example of the data processing which occurred in the background is given below.

a) b)

Figure 5.4. Association data for report point A1 expressed as % response of the reference inhibitor (Indinavir). (a) Data arranged alphabetically by substance code name. (b) Same data (negative values removed for clarity) reordered by date of experiment. Individual experiments are separated by the marks below the x-axis.

Figure 5.4a shows some early results for A1 expressed as the relative % response of Indinavir on the y-axis. At this stage in the investigation, most compounds had been measured in duplicate and all data points for each compound are shown in the figure.

49 A completely unexpected results was the number of compounds which expressed very high association data. Over 10% of the measurements reported A1 values over 200% of the average Indinavir signal which would roughly correlate to a Ki in the high femtomolar range. That estimate is from an extrapolation far beyond the range later determined for the full dataset (Figure 5.3) but it's good enough to indicate a problem since none of the compounds from this 10% had measured Ki values much below the nanomolar range.

Reordering of the data by the time of the experiment (Figure 5.4b) showed a definite pattern: the second and third experiments produced the suspicious results. Identification of this troubled region of the dataset allowed us to identify the specific problem (high refractive index of the bulk solution in two plates). Simply eliminating all data with high A1 and A2 values would certainly not be reasonable since we would have to presuppose that nothing significantly better than Indinavir could be found. Assuming that all duplicates with a small relative error to be dependable would also have been wrong since some of the bad data had been duplicated. Elimination of the association data for these two experiments was certainly the most prudent course of action.

5.4 QUANTITATIVE STRUCTURAL ANALYSIS OF KINETICS DATA

The commonly reported steady-state inhibition constant, Ki, is a standard (though not exclusive) determinant for whether a compound can become a lead or rejected. The related equilibrium dissociation constant (KD) is a composite term of the association

(kon) and dissociation (koff) rates: KD = koff / kon. These rates are independent quantities which not only describe different aspects of binding but behave differently in different environments.290,291,288 The pharmaceutical importance of breaking affinity into its constituent parts has recently been discussed in the context of HIV PR.292,287

The work presented in Section 5.2 was a broad screen of PR inhibitors so only a few reporter points for association and dissociation were used. A more detailed study of a

50 diverse subset of these compounds was selected for a more detailed kinetics study.293

In that study, the kon and koff values were used to make SAR analyses of the inhibitors.

Some other studies have noted correlations of kon and/or koff to some chemical descriptor and the idea of using this in a QSAR study has been presented294 and carried out.295,296 It is believed that the work presented in this chapter is the first

229 application of a 3D-QSAR technique, in this case, CoMFA, to the study of kon and koff.

The dataset for the CoMFA study consisted of 34 compounds. These were split into training (22) and test (12) sets via an experimental design based on several chemical descriptors. It was hoped that the chemical design would help insure a good coverage of some features of the chemical space without making biased selections.

The derived CoMFA model produced a reasonably good q2 of 0.44 for the dissociation rate (Figure 5.5a). The model reproduced the test set data with a predictive correlation 2 2 coefficient (rpred) of 0.59. The q for the association rate was a much less impressive 0.25 which was close to the point of being useless (Figure 5.5b).231 The test set gave 2 correspondingly poor rpred of 0.14. A bit disappointed with these results, we embarked on a campaign to find better models.

Figure 5.5. Plots of actual versus calculated (a) pkoff or (a) logkon for the training (crosses) and test (circles) sets of the CoMFA models with default settings. The dashed-lines mark one-to-one ratio for reference only.

51 5.5 COMBINATORIAL COMFA

CoMFA, as implemented in the Sybyl molecular modeling package,297 defines many adjustable parameters. Many CoMFA studies have reported adjusting some of these parameters to produce models of significantly better (and worse) statistical quality.298,299 In the hopes of finding better CoMFA models for the association and dissociation rates, we used the experience gained from the binding mode search presented in Section 4.5: we used a script to automate the calculations. Instead of varying the binding modes, we varied the CoMFA parameters.

Our first step was a short search to position the grid over the compounds. The default grid was adjusted in 0.5 Å increments along the x, y and z axes independently. A total of 189 grids were used. Leave-one-out validation was used for all models. The 2 2 improvement of q scores for koff gave a respectable 0.59 (up from 0.44) and the q of

0.37 for kon became at least acceptable. Still not satisfied with these results, the search proceeded with other adjustable parameters. The five best grids (as ranked by their q2 score) were carried over to the next stage.

Table 5.1. Adjusted CoMFA parameters. Variable Values FIELD_TYPE ELECTROSTATIC, STERIC, BOTH STERIC_ENERGY_MAX 80, 60, 45, 30, 15, 5 ELEC_ENERGY_MAX 80, 60, 45, 30, 15, 5 VOLUME_AVG_TYPE NONE, BOX SWITCH_FCN NO, YES HBOND_FCN NO, YES TRANSFORM NONE, INDICATOR, SQUARED

The COMFA parameters adjusted in this search appear in Table 5.1. The variable names correspond to those used in the "tailor comfa" settings of Sybyl and the technical explanations for them can be found in the Sybyl manual.297 All of these

52 variables correspond to settings which are accessible through the normal, interactive, graphical mode of Sybyl (assuming the appropriate modules are available).

Figure 5.6. Plots of actual versus calculated (a) pkoff or (a) logkon for the training (crosses) and test (circles) sets of the CoMFA models with optimized parameters. The dashed-lines mark one-to-one ratio for reference only.

Systematic variation of these variables, avoiding the disallowed or unproductive combinations, resulted in the generation of 348 CoMFA models for each of the five grid files and both koff and kon to give a total of 3480 CoMFA models, each with leave- 2 one-out validation. The q score for koff was now a very nice 0.72 (Figure 5.6a) and kon improved to 0.48 (Figure 5.6b); higher than the default model for koff.

While q2 is a standard measure of the quality of a model, it certainly doesn't match an 2 external prediction of a test set. The rpred for the test set against the improved model for koff was 0.60. Compared to the default model's 0.59, this is certainly no real improvement. The test set was reasonably well predicted for the default CoMFA model so at least the model didn't do any worse after parameter optimization. 2 Substantial improvements were needed for kon which had a rpred of 0.14 for the default 2 model. Unfortunately, the substantially improved model didn't fare much better: rpred = 0.20. At least for this dataset, the significant improvements in q2 did not translate into a tangible benefit for prediction of the test set.

These results (as some other studies have shown)263,300,299 should at least be a warning 2 2 against the over reliance on q . For example, the q values for koff span a range from -

53 0.27 to 0.72 for the various combinations of adjustable parameters (Figure 5.7). This can be interpreted in at least two ways: (a) the default CoMFA settings are not necessarily the best parameters for all models, or (b) a given set of parameters can produce any of an incredibly wide range of values.

Figure 5.7. Crossvalidated correlation coefficients (q2) for the 1740 CoMFA models calculated for the dissociation data (modelled as pkoff).

It should also be stressed that the work presented here represents only a single dataset 2 (albeit with two y variables) so the generality for rpred is far from certain. Further research into this question for other datasets as well as a deeper analysis of the q2 data is in progress.

5.6 COMPUTATIONAL DETAILS

Varying the CoMFA parameters combinatorially is a slow, "brute force" search. Luckily, the calculation of 3480 models was accomplished in about 7½ hours. This speed is partly thanks to the fast alternative to a full PLS301 calculation called SAMPLS (Sample-distance Partial Least Squares)302. Sybyl Programming Language (SPL) and UNIX shell scripts were written to manage the SAMPLS calculations. Minor adjustments to the SAMPLS control script

54 ($TA_ROOT/comfadv/tables/sampls.core) were necessary since the global Sybyl variables QSAR_STDERR and QSAR_CROSS_R2, reporting the standard error and crossvalidated correlation coefficient (q2) for each component, were not being used. The sampls.core script was altered to set these global variables to the local values of std_err and cross_r2, respectively. The implementation of SAMPLS in Sybyl also suffers from a small memory leak which becomes problematic after the calculation of a few hundred CoMFA models. A UNIX shell script was used to restart Sybyl after about every 100 CoMFA calculations.

55 6 EMPIRICAL FORCE FIELD PARAMETERIZATION

This chapter is a brief description of some unpublished results of direct relevance to Chapter 4 of this thesis. The preparation of a manuscript is in progress.

As was mentioned in Section 4.3, the sulfamide moiety R2NSO2NR2 is not well parameterized in the available empirical force fields. As a result of this missing data, the molecular modelling studies of the cyclic sulfamide HIV-1 PR inhibitors (Figures 4.4b, 4.7 and 4.8) presented in Chapter 4 were quite limited. Basically, we relied on some X-ray structures to align the inhibitors and allowed the sulfamide ring to explore at most one alternative conformation (Figure 4.6b). While the available X-ray data has so far indicated a single, consistant ring conformation, it is difficult to assume that no other low energy conformations exist in solution.

The ability to accurately perform an extensive conformational analysis, as has been reported for cyclic ureas,303 could help answer this question. Furthermore, unrestrained energy minimization would open up the possibility of using some alternatives to CoMFA for activity prediction.304 In the hopes of being able to eliminate these limitations, work has been initiated to make new parameters for the AMBER* force field248 of MacroModel.247

Several routines to fit new parameters for existing FFs have been described using neural networks,305 simplex optimization306 or genetic algorithms.307 We have used the procedure of Norrby and Liljefors.308 Their technique uses QM frequency calculations at non-stationary points similar to the procedure309,251 used to derive some of the most generally successful FFs in common use.226

In contrast to some of the more simple FF parameterization procedures,310 this method allows the inclusion of experimental data. Crystallographic data for several sulfamide derivatives (Figure 6.1) were used as models for energy conformations (listed here by

56 CSD code): CITSON,311 CITSON10,312 DMAMSO,313 FIKHEM,314 FIKHIQ,314 GABGIZ,312 GABGUL,312 KIBRAO,315 KIKSEC316 and SIKFUN250. Spectroscopic data for tetraalkylsulfamides was used as secondary data source.317-319

O O O O O O S N S S N N N O O N N OO S O S N N N N O O O CITSON & Bu O Br CITSON10 DMAMSO FIKHEM GABGIZ GABGUL

OO N N S OO S O N N N N N O O N S S O N O O NH N N Cl HN N N HO KIBRAO KIKSEC SIKFUN

Figure 6.1. X-ray structures used to guide force field parameterization.

DFT calculations. As done in Section 4.3, B3LYP/6-31G* with Gaussian94 was used for the QM calculations. Tetramethylsulfamide (DMAMSO in Figure 6.1) was used as the model compound for the cyclic sulfamide inhibitors like AHA-006 (Figure 4.4b). Using a smaller model compound, e.g., unsubstituted sulfamide, might not correctly reproduce the electronic character of the inhibitors. Using a larger molecule as the model, e.g., the truncated sulfamide ring modelled in Section 4.3 (Figure 4.6b), would be computationally more expensive and possible not sufficiently flexible to allow a good fitting of the torsional data.

The individual iterations of the geometry optimization are shown in Figure 6.2a. The small jumps in the energy near the beginning of the optimization are not strange and even the large spike to 116 kJ/mol at the 34th iteration isn't so unusual. What is troubling is that the optimization never converged. As seen in the expansion of the last

57 part of the optimization run (Figure 6.2b), the energies are oscillating. In Gaussian94, convergence for geometry optimizations are reached only the forces on the atoms (maximum and RMS) as well their displacements (maximum and RMS) are below some predefined limits. In this calculation, the forces were well below the cutoffs but the structure couldn't stabilize. Since the oscillations were of such low amplitude, less than 10 J/mol (in the microhartree range), this nearly optimized structure was used in the next stage of the calculations. a) b)

Figure 6.2. Progress of the B3LYP/6-31G* geometry optimizations plotted as the number of iteration versus the energy in kJ/mol relative to the lowest energy found. (a) Complete course of the optimization where the gaps at iteration 29 and 42 represent restarts. High energies are truncated for clarity. (b) Detailed view near the end of the optimization.

The torsional space was explored with DFT calculations on a collection of 16 rotamers. Frequency calculations away from the local energy minima where performed to gather detailed information of the potential energy surface.

Parameterization. With all of the necessarily X-ray, spectroscopic and QM data in hand, it was time to fit it all together into a few improved FF parameters. The default AMBER* FF definition file, amber.fld, was modified with some reasonable guesses for the parameters which are to be added or modified. We added bond stretch terms for S–N, where the S is also bound to two N, and for S=O, where the S is also bound to an N and an O. In other words, the bond stretch terms defining the sulfamide moiety

58 which where poorly parameterized in the default FF. Parameters defining the appropriate bond angles, torsions and improper torsions where similarly defined and given starting values.

Fitting the experimental and calculated data into AMBER* to make an improved, but still internally consistant, FF was accomplished through the collection of programs and scripts of Norrby and Liljefors.308 The scripts are partially automated but the user maintains some control over the procedure through efficient application of either the simplex or newton-raphson optimizations based on the progress of the convergence.

Results of the parameterization. Starting from the (nearly) optimized QM geometry, a dihedral angle drive (relaxed potential energy scan) of one of the N–S bonds was performed both with the default AMBER* FF and with B3LYP/6-31G*. As shown in Figure 6.3, the torsion energy curve for AMBER* exhibits a roughly negative correlation to the B3LYP/6-31G*. The minima in the AMBER* are near the maxima for B3LYP/6-31G*. Figure 6.4 presents the results of the same dihedral angle drive with the newly parameterized AMBER*. While the fit isn't perfect, all of the features of the B3LYP/6-31G* curve are reproduced.

59 Figure 6.3. Drive of one of the S–N torsions using the default AMBER* force field in MacroModel 5.5 (solid line) and with B3LYP/6-31G* in Gaussian94 (dashed line).

Figure 6.4. Drive of one of the S–N torsions using the reparameterized AMBER* force field (solid line) and with B3LYP/6-31G* in Gaussian94 (dashed line).

60 Of more practical significance than a reproduction of QM results for a single torsion is a test of how well the new FF can perform on molecules. Reproduction of the X-ray structure reported for tetramethylsulfamide model compound (DMAMSO) isn't a perfect test since the structural determination for this molecule was not of particularly high quality (RFAC = 0.85) so a comparison to SIKFUN (RFAC = 0.32) was made instead.

Starting from X-ray coordinates, geometry optimizations of SIKFUN were calculated using either default AMBER* or the reparameterized version. As summarized in Table 6.1, the new, reparameterized AMBER* parameters were able to maintain the X-ray geometry of SIKFUN. The ∆E, difference in energies of the minimized and X-ray geometries, for the new FF were better than achieved with the default parameters but still not perfect. The new parameters may need further improvement.

Table 6.1: Application of old and new AMBER* to SIKFUN. AMBER* RMS ∆E default 0.45 74.8 kJ/mol reparameterized 0.05 52.6 kJ/mol

Work is in progress to perform more rigorous testing of the new force field parameters on full-sized inhibitors with the protease active site.

61 7 CONCLUDING REMARKS

X-ray analysis of a sulfamide derivative of previously studied cyclic urea inhibitors revealed a unexpected binding mode. The central ring was twisted to flip what would seem to be the P1' into S2' and the P2' into the S1' pockets of the protease. Computational studies were initiated to help confirm and understand the nature of this result. Ab initio calculations were performed to estimate the relative energies of the symmetric and nonsymmetric conformations. Molecular modelling was used to help design compounds to test the robustness of the nonsymmetric conformation.

A set of nonsymmetric cyclic sulfamide inhibitors was investigated. These compounds were designed to be better adapted to the consistently observed nonsymmetric binding mode. Determination of which side of the inhibitor would adopt the flip turned out to be nontrivial. CoMFA models were derived for each combination of flips in order to help guide the SAR. A conventional CoMFA model was subsequently made to aid in the design of new inhibitors.

Modelling of the cyclic sulfamide inhibitors is impeded by the lack of high quality empirical force field parameters. Work is in progress to development improved parameters and preliminary results look promising.

CoMFA models were derived to explain the correlation between structure and binding data for a set of kinetics data. An extensive and systematic investigation of the adjustable CoMFA parameters was performed to search for better models. Statistically improved models were obtained and the practical utility of this procedure is discussed.

62 8 ACKNOWLEDGEMENTS

I wish to express my sincere gratitude to:

Docent Anders Karlén, my supervisor, for his great patience, accessibility and insight into complex issues. Especially appreciated was his role in helping me to simplify my writing when I'd failed to eschew obfuscation. Professor Anders Hallberg for support throughout this study by providing the excellent working facilities and through his active encouragement. All of the chemists who worked so hard to design and synthesize the compounds investigated in this study. Thanks for giving me something to calculate but mostly for lending relevance to this study. Special thanks to the prolific accomplishments of Drs. Johan Hultén and Mathias Alterman. Dr. Mats Larhed and Docent Uno Svensson for their expert advise both in general chemistry and diverse topics like auto maintenance and Swedish etymology. Docent Helena Danielson and her associates for providing the kinetics and affinity data without which my CoMFA and QSAR models would obviously have been impossible. Docent Torsten Unge and associates for solving so many X-ray structures. Docent Björn Classon and Medivir for the opportunity to work with large datasets. The PDC at KTH for computational support and facilities. Special thanks to Nils Smeds who worked so hard on MacroModel when we were the only users. Dr. Susanne Winiwarter, computational cohort, for her early help with SPL, many technical discussions, friendship and for being such a good officemate. Comp Chem students Shane Peterson and Christian Sköld for the promise of interesting projects together. Fredrik (Frax) Ax for the friendship and many interesting conversations. Darn shame you left us for the real world. Anna Ax née Karlsson for both her valuable friendship and willingness to discuss ideas regarding synthesis and computational analysis. Dr. Tero Linnanen for his view from the bench.

63 Robert Webster for always being my best friend. Marianne Åström, Gunilla Eriksson and Arne Andersson for their skillful administrative and technical assistance. Former advisors: Prof. R. P. Cuila for sparking my interest in organic chemistry; Dr. Dan Kubose for good laboratory practice; Prof. Anders Liljas for enzymology and protein crystallography; Prof. Daniel S. Kemp for synthetic organic chemistry; Prof. W. Todd Wipke for computer programming and computational chemistry. Kaisa for dragging me to this winter wonderland but mostly for the love and support. Я тебя люблю! Daughters Sonia and Ellen for their barely controlled (im)patience of my work schedule. Thanks to all three for making everything worthwhile. Sheila, my mother, for encouraging me to follow my own interests. Siblings Michelle and Ben for helping me to understand and appreciate children and then for growing up into real people. Meaw, Popo, Jr, Gail, Carol and cousins for the sense of family and for the memories of the past. The Kingdom of Sweden for providing a safe and nurturing environment for my family, for allowing me to study and work here with essentially the same rights as a citizen and for the environment of tolerance of my foreign habits and values. This place could still be improved with California weather and access to better Salsa but at least I can scratch together everything needed for an authentic Thanksgiving meal.

This investigation was carried out at the Division of Organic Pharmaceutical Chemistry, Department of Medicinal Chemistry, Faculty of Pharmacy, Uppsala University, Sweden. Financial support was obtained from the Swedish National Board for Industrial and Technical Development (NUTEK), the Swedish Foundation for Strategic Research (SSF) and Medivir AB, Huddinge, Sweden.

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