Richard Morphy gained his BSc (1995) and PhD (1989; supervisor: Prof. David Parker) in Chemistry from University of Durham 1989-1995: Medicinal chemist at Celltech, Slough, UK working on oncology and inflammation projects 1995-to date: Section head / Senior Research Fellow at Organon/SPRI/MSD, Newhouse, Scotland working on CNS and CV projects. He has an extensive track record of research, publications and presentations in the area of multi-target drug discovery (MTDD). John Harris gained his BSc.(Chemistry, 1999) from University of Exeter; PhD (Chemistry, 1974) from Queen Mary College, University of London (Supervisor: Prof. B.C.L. Weedon FRS) Postdoc study 1973-1975 with Prof. C.W.Rees at University of Liverpool. 1975 – 1982 Medicinal Chemist at Wellcome Labs, Beckenham, working on cardiovascular projects. 1983-1988 Principal Scientist 1989 – 1995 Head of Cardiovascular Area, Wellcome UK. 1996 – 2008 Founder and CSO of BioFocus (now division of Galapagos); 2009 – to date, independent pharma/biotech consultant. Comprehensive track record of research, publications and presentations in the areas of enzyme inhibitors, prostaglandins, compound library design, kinase drug development in oncology, inflammation and CNS, and multi-targeted drug discovery.

Designing Multi-Target Drugs

By J. Richard Morphy, C. John Harris

The Royal Society of Chemistry

Copyright © 2012 Royal Society of Chemistry
All rights reserved.
ISBN: 978-1-84973-362-5

Contents

Chapter 1 Simple Drugs Do Not Cure Complex Diseases: The Need for Multi-Targeted Drugs Jorrit J. Hornberg, 1,
Chapter 2 Clinical Need and Rationale for Multi-Target Drugs in Psychiatry Mohammed Shahid, 14,
Chapter 3 Drug Molecules and Biology: Network and Systems Aspects Malcolm P. Young, Steven Zimmer and Alan V. Whitmore, 32,
Chapter 4 Chemoinformatic Approaches to Target Identification Elisabet Gregori-Puigjané and Michael J. Keiser, 50,
Chapter 5 Designing Multi-Target Drugs: In Vitro Panel Screening – Biological Fingerprinting Jonathan S. Mason, 66,
Chapter 6 Phenotypic and In Vivo Screening: Lead Discovery and Drug Repurposing Christopher A. Lipinski, 86,
Chapter 7 Target/s Identification Approaches – Experimental Biological Approaches Giulio Superti-Furga, Kilian Huber and Georg Winter, 94,
Chapter 8 Historical Strategies for Lead Generation J. Richard Morphy, 111,
Chapter 9 In Silico Lead Generation Approaches in Multi-Target Drug Discovery Xiaohou Ma and Yuzong Chen, 130,
Chapter 10 The Challenges of Multi-Target Lead Optimization J. Richard Morphy, 141,
Chapter 11 Combination Agents Versus Multi-Targeted Agents – Pros and Cons Jose G. Monzon and Janet Dancey, 155,
Chapter 12 The Discovery of Lapatinib Karen E. Lackey, 181,
Chapter 13 Identification and Optimization of Dual PI3K/mTOR Inhibitors Andreas Karlsson and Carlos García-Echeverría, 206,
Chapter 14 Discovery of HDAC-Inhibiting Multi-Target Inhibitors Xiong Cai and Changgeng Qian, 221,
Chapter 15 Targeting Protein–Protein Interactions: Dual Inhibitors of Bcl-2 and Bcl-xL Michael D. Wendt, 243,
Chapter 16 Discovery of the Anti-Psychotic Drug, Ziprasidone John A. Lowe, III, 263,
Chapter 17 The Rational Design of Triple Reuptake Inhibitors for the Treatment of Depression Robert J. Weikert, 270,
Chapter 18 Discovery of Multi-Target Agents for Neurological Diseases via Ligand Design Maria Laura Bolognesi, Carlo Melchiorre, Cornelis J. Van der Schyf and Moussa Youdim, 290,
Chapter 19 Designing Drugs with Dual Activity: Novel Dual Angiotensin II and Endothelin Receptor Antagonists Natesan Murugesan, 316,
Chapter 20 Ethyl Urea Inhibitors of the Bacterial Type II Topoisomerases DNA Gyrase (GyrB) and Topoisomerase IV (ParE) Stephen P. East, Lloyd G. Czaplewski and David J. Haydon, 335,
Epilogue, 353,
Subject Index, 356,

CHAPTER 1

Simple Drugs Do Not Cure Complex Diseases: The Need for Multi-Targeted Drugs

JORRIT J. HORNBERG

H Lundbeck A/S, Ottiliavej 9, 2500 Valby, Denmark

Email: JJH@Lundbeck.com

1.1 Introduction

The contents of this book provide a comprehensive overview of the field of polypharmacology and modern approaches to identify drugs that hit multiple targets, including a number of case studies. But why do we actually need such multi-targeted drugs? This introductory chapter aims to answer that question. First, there is clearly a need for better and safer drugs in the clinic and also to improve output (productivity) of drug discovery and development in general. Second, many diseases with unmet medical needs are in essence complex and multi-factorial. I will discuss two disease areas, cancer and rheumatoid arthritis, to exemplify this complexity. Systems biology and network control analysis have shown that the systems underlying complex diseases are robust against perturbations and are always controlled by more than one biochemical process. Therefore, aiming to hit multiple targets is a better strategy than to hit a single target. Finally, though polypharmacology is naturally associated with toxicology and off-target side effects, it can be argued that multi-targeted drugs, when rationally designed, can actually have a larger therapeutic window than those hitting a single target and thus prove to be safer drugs.

1.2 The Need for Better and Safer Drugs

There are two main reasons why we need better and safer drugs.

Firstly, there is the unmet medical need in the clinic. Patients need safe cures and complex diseases are difficult to cure. Cancer survival rates, for example, are still lower than desired, roughly 50–65% in Europe and the US, making it a leading cause of death, responsible for almost 25% of all deaths in the US. Other examples include autoimmune disorders, some cardiovascular diseases, diabetes and neurodegenerative diseases. The incidence of some of these diseases is expected to increase with the increasingly ageing population. Dementia, for instance, affects almost 1% of those at 60–64 years of age and that number doubles for each subsequent 5-year cohort to 25–33% of those ≥ 85 years of age. Furthermore, the incidence of serious adverse drug reactions in hospitalized patients is so high that it ranks as the 4th–6th leading cause of death.

Secondly, the low success rate of drug development calls for better and safer drugs. In the past few years, an average of only ~20 new drugs were approved annually. This is the result of the high attrition rates in clinical development: about 90% of all new drugs fail after first-in-human testing, varying from 80% for cardiovascular diseases to 95% for cancer. The main underlying causes were identified to be lack of efficacy and poor safety (toxicology and clinical safety), each accounting for ~30% of all failures.

One may therefore argue that we need to hit better targets. However, going after novel targets has, in itself, not proven to be a particularly successful strategy for drug development. Attrition of candidates with a novel mechanism of action is higher than average. In addition, many diseases with unmet medical needs are complex and multi-factorial. Therefore, an approach to hit multiple targets may be more successful. The next two sections of this chapter discuss the complexity of cancer and rheumatoid arthritis.

1.3 Cancer

Two decades ago, Fearon and Vogelstein proposed their genetic model for colorectal tumorigenesis. From comparing cells from multiple stages of colorectal cancer, it became apparent that, at each stage, cells had acquired at least one additional mutation, compared to the previous stage. The fact that carcinogenesis is a multi-step process requiring multiple sequential mutations has since been confirmed many times, e.g. by similar models for other types of cancer and by the artificial creation of tumour cells by introducing defined genetic alterations. Besides mutations that change the structure and function of a gene product, (epi)genetic alterations which influence gene expression also contribute to carcinogenesis, such as gene amplification, changes in DNA methylation and histone acetylation, and the functioning of micro-RNAs. The list of genes which are causally implicated in cancer via genetic alteration currently contains 436 genes. Though some genes are frequently mutated in many cancer types, it is rather a combination of low frequency mutations that drive the cancer phenotype and they differ per cancer type. This (epi)genetic heterogeneity of cancer presents a major challenge. Disturbances of the signal transduction pathways in which most of these cancer genes function leads to the so-called ‘hallmarks of cancer’, including evasion of apoptosis and growth control, self-sufficiency in growth signals, induction of angiogenesis, the ability to metastasize, evade immune surveillance and, indirectly, stress phenotypes. It is important to recognize that cancer is a multi-factorial disease already at its origin (the genetic level) and in its essence (the hallmarks). But this is merely where the complexity starts.

The information flow through signalling pathways that ultimately constitute these hallmarks is highly complex. The many sequential steps in a pathway encompass many different biochemical processes, such as protein binding (e.g. recruitment of pathway components by scaffolds, binding of ligand to receptor, receptor dimerization, transcription factor binding) and enzymatic reactions (e.g. phosphorylation, methylation, acetylation, ubiquitination). The output of a signalling pathway does not follow the input in a linear fashion. In part, this is caused by non-linear kinetics of biochemical reactions and different expression levels of pathway components. Signalling is also subject to spatio-temporal control and macromolecular crowding, since pathway components can be physically separated or highly concentrated locally. Moreover, about a decade ago, the concept that signal transduction pathways have a linear architecture had to be abandoned. First, the topology of regulatory circuitry, such as negative feedback loops, appeared to be a recurrent theme in signalling networks. Second, pathways are so highly interconnected by direct interactions (e.g. via phosphorylation), indirect regulation (e.g. via gene expression) or by sharing pathway components, that they form complex signalling networks rather than linear pathways. This network structure itself can give rise to new ’emergent properties’ or ‘systems behaviour’.

Beyond the multitude of contributing factors on the genetic level and in signalling networks, the complexity extends on the supra-cellular level. The population of cancer cells in a tumour is heterogenic, e.g. with respect to their state of differentiation. Cells communicate with each other either by direct interaction or by stimulating each other in paracrine growth factor loops. Targeting those loops is a strategy for treatment of certain cancers. And within the microenvironment, complex interactions between tumour cells and stromal cells, like paracrine growth factor networks, stimulate tumour progression. Then, there is the interaction with the host immune system. On the one hand, tumour cells can secrete cytokines which trigger macrophages to differentiate to a subtype that actually promotes tumour progression. On the other hand, immune cells can clean up tumour cells, which makes the therapeutic strategy to stimulate the host immune system to launch an immunological attack on the tumour very promising. A further example of an important supra-cellular interaction is the activation of angiogenesis. Under hypoxic conditions, tumour cells secrete pro-angiogenic growth factors, thereby stimulating the proliferation of endothelial cells. This leads to new blood vessel formation and provides the tumour cells with access to more oxygen. If tumours do not trigger this ‘angiogenic switch’, they will not be able to grow and will remain dormant.

In summary, it can be concluded that a combination of multiple genetic alterations that affect multiple processes give rise to the development of cancer.

1.4 Rheumatoid Arthritis

A similar conclusion can be drawn from the biology of many autoimmune disorders, such as rheumatoid arthritis (RA). This systemic disease is characterized by joint inflammation and subsequent cartilage and bone destruction. The development of the disease occurs roughly in three steps: (i) the onset of a modest local inflammation in the synovium by the innate immune system; (ii) triggering of an adaptive immune response by the recruitment and activation of immune cells, leading to full-blown inflammation; and (iii) the invasion of the cartilage and increased bone resorption leading to bone destruction.

Many different cell types are involved during this process. Mast cells produce pro-inflammatory mediators, such as cytokines, and also tissue destructive proteases. They contribute to the initiation of the adaptive immune response by recruiting T-cells, influencing T-cell skewing and antigen presentation. Also dendritic cells, in their function as antigen-presenting cells, are thought to play a role in initiation of the adaptive immune response. Macrophages produce a large variety of cytokines and chemokines. Their abundance and activation in the synovium correlates with the severity of RA. Abnormal lymphocyte function is thought to be a central part of RA pathology. T-cells mediate both the recruitment and the activation of other immune cells by direct cell–cell contact and cytokine production. B-cells produce self-reactive antibodies, function as antigen-presenting cells and secrete cytokines. Fibroblast-like synoviocytes in the synovial intimal lining also play a key role by producing cytokines that perpetuate inflammation and proteases that contribute to cartilage destruction. They develop a unique aggressive phenotype that increases invasiveness into the extracellular matrix and further exacerbates joint damage. Another important cell type for bone destruction are osteoclasts. Joint degeneration is ultimately mediated by a disturbance in bone homeostasis, which is normally kept by balancing bone formation (by osteoblasts) and bone resorption (by osteoclasts). In RA, bone loss is due to excess bone resorption by osteoclasts, which are activated by inflammatory factors, and simultaneously, bone formation by osteoblasts is also impaired at erosion sites.

These many different cell types (and their subtypes) all contribute to RA and they communicate with each other by direct cellular contact and by an extensive extracellular network of cytokines and chemokines. Factors like TNFα, IL-1β, IL-2, IL-6, IL-7, IL-8, IL-15, IL-17, IL-18, IL-21, IL-22, IL-23, GM-CSF, MMPs, RANKL and RANTES function in autocrine and paracrine networks that mediate the onset and propagation of the inflammation, as well as bone destruction. The central role of cytokines in RA pathology is clearly evidenced by the relatively successful application of biologics that block cytokine function to treat the disease.

Zooming in on the individual cell types presents a further level of complexity: the structure and function of signal transduction pathways mediating the inflammatory response and regulating cell proliferation, survival and differentiation, and expression of aforementioned extracellular factors. The activation of lymphocytes often requires two signals. T-cell activation occurs after engagement of the T-cell receptor (TCR) with its cognate peptide–major histocompatibility complex (signal 1) and subsequent engagement of co- stimulatory molecules (signal 2). This ‘second signal’ contributes to T-cell activation by promoting proliferation, survival and effector function. A multitude of factors are involved in processing the signal from the TCR and activating downstream signalling pathways. Similarly, B-cell activation upon stimulation of the B-cell receptor (BCR) requires co-stimulation for the development of complete effector function, and a complex cascade is involved in propagation of the signal. The key downstream signalling pathways in inflammation are similar to, overlap with or are often even the same as the signalling pathways involved in cancer, e.g. JAK-STAT, NF-kB, MAP kinase pathways. As discussed above in Section 1.3, these pathways are highly complex with respect to the large number of signalling molecules, the different nature of the reactions they are involved in, the structure of the pathways, with cross-talk and feedback loops, etc. Many kinases in these pathways are currently being pursued as drug targets for treatment of RA.

In conclusion, RA is a complex disease, which depends on the combined action of many cell types and multiple factors at the supra- and intra-cellular levels.

1.5 Control of Complex Biological Systems

As complex diseases, such as cancer and rheumatoid arthritis, are indeed dependent on so many factors, it is a major challenge to produce successful treatment strategies. Systems biology is needed to integrate the available knowledge and develop comprehensive understanding of how systems behaviour depends on the components of the system and their interactions. Substantial advances in understanding (and prediction) have been achieved by integrating computational modelling with quantitative experimental data on molecular and cellular networks, in particular in the field of cancer systems biology. On the basis of network models, it has been proposed that partial inhibition of multiple drug targets is more effective than full inhibition of a single target. This can be explained by the intrinsic robustness (the ability to maintain homeostasis) of living systems. Biological networks are built up with a high degree of redundancy and a small fraction of essential components. In order to modulate the phenotype of a complex and robust disease system, several perturbations of non-essential components must be combined. Network analysis can then further aid to identify successful combinations of drug targets. It is essential to recognize that the robust nature of complex diseases is caused by their network properties and it is therefore the network that should be targeted, rather than isolated parts.

A similar lesson can be drawn from the application of metabolic control analysis (MCA). MCA quantifies the extent to which individual reactions or network parts control the entire reaction network, such as the control of a kinase on downstream phosphorylation, the control of a signalling pathway on cell proliferation, or the control of a particular cell type on disease pathology. In that way, MCA aids in the selection of drug targets, based on the magnitude of their control. Applying MCA to computational models of signal transduction indeed allowed for rank-ordering individual reactions: some reactions exert more control than others. Perhaps more surprisingly, it was also found that control tends to be distributed over more than a single reaction. In other words, there was no single rate-limiting step. One can thus argue that multi-targeted drugs will be more effective than mono-targeted drugs.

1.6 Safety of Multi-Targeted Drugs

As mentioned above, where ~90% of all drug development projects fail after first-in-human testing, ~30% do so because of toxicology or clinical safety criteria. Since many animal toxicity studies are done in pre-clinical development, the attrition related to safety/toxicology issues of development candidates produced by drug discovery research is even higher. Serious adverse drug reactions leading to attrition and drug withdrawal manifest themselves in a variety of different ways and organs, in particular in cardiovascular side effects and in liver toxicity. Because of its association with toxicity (often referred to as side effects), polypharmacology has long been considered undesired. However, there are some arguments that polypharmacology in itself does not necessarily have to be associated with toxicity and that multi-targeted drugs can actually have a better e?cacy/safety ratio than mono-targeted drugs. The underlying causes for safety/toxicology-related issues can roughly be grouped into three categories.

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