Ebook: Towards Drugs of the Future

“The Solvay Pharmaceuticals Conferences: where industry meets academia in a search for novel therapies”

The Big Picture of Pharmaceutical Industry is Changing

To capture growth opportunities of the future the pharmaceutical industry must change the way it operates [1]. In a new regulatory and stakeholder environment the current business model is economically unsustainable and incapable to deliver expected output [1,2]. The number of novel medications in the global industry pipeline is insufficient to suffice demand of the markets, the financial performance of several companies is declining, sales and marketing costs are rising, regulatory constraints are increasingly challenging and overall reputation of industry is tarnishing. Unmet medical needs must be better addressed and tangible benefits need to be exposed in order to be rewarded by healthcare payers [3]. In addition, the focus is shifting from simple treatment of the ill to preventive measures and health management. To achieve this fundamental change industry will shift investment again towards scientific approaches, in life sciences and towards (pharmaco)economics, and away from conventional sales and marketing.

A new model may be described as follows: The linear phase development process will be replaced by in-life testing and life licensing, the initial clinical studies will become smaller but will be more focused and the results will be shared with regulators when testing. Safety and efficacy data will be globally shared by regulatory agencies which may lead to one global regulatory system and a larger safety data base. Integrated packages of medicines and services will dominate pharmaceuticals market and soon disease management may create more value than medications themselves. The distribution channels of medicines will be reinforced by enabling technologies allowing for e-dispensing of medicines directly to patients from online pharmacies [3]. Such global challenges require pharmaceutical industry to rethink and adopt the current business model in order to contribute value to the healthcare systems of the future [4].

To retain key position of chemistry in the future pharmaceutical industry the way chemists work must change. During the last two decades paramount investments in mass screening technologies and in combinatorial chemistry were made, knowledge in synthetic chemistry expanded, computer-assisted drug design matured but output of medicinal chemistry, measured as successful launches of novel medicines, declined indicating that novel technologies are used in old paradigms [1,5].

The challenge is to ensure that new chemistry is driving progress of pharmaceutical industry by means of novel technologies in which system biology approach or target approach is computer simulated, testing is performed in silico on rat or human digital models and synthesis of molecules is restricted to intelligent synthesis of treatments based on simulations. Implementation of these technologies will reduce costs and shorten timelines because only intelligent treatments will be tested in vitro and in vivo and portfolio decisions will be based on in silico tests while pharmacology and toxicology tests on animals will only be performed on candidates selected as intelligent treatments [2,6].

The Ninth Solvay Pharmaceuticals Conference on Medicinal Chemistry held in Garmisch-Partenkirchen (Germany) September 26–28, 2007 addressed newest development in synthetic and medicinal chemistry with the emphasis on the challenge of the future. The scientific focus of this conference centered on the involvement of emerging technologies in chemistry on the speed of drug discovery and on the aspects of drug finding including intelligent biology and rational drug design.

W. Cautreels, C. Steinborn, L. Turski

References

[1] J. Cacciotti and B. Shew. Pharma's next top model: slimmer business models. Pharm. Exec. 3 (2006) 28–30.

[2] EFPIA. The Pharmaceutical Industry in Figures (2008).

[3] S. Arlington. A vision of 2020. Pharm. Exec Europe 10 (2007) 26–28.

[4] I. Massey. An alternative drug development model. Pharm. Exec. Europe 9 (2008) 24–25.

[5] L. Turski. New business models required for today's drug development. Conceptuur 44 (2005) 6–7.

[6] L. Turski. A passion for progress. GIT Lab. J. 8 (2004) 37.

The definition of a ‘Target’ for drug design; the selection by a pharmaceutical company of a particular Target; and the responsibilities of the pharmaceutical industry to Society as a whole are briefly discussed.

The application of newly developed thermodynamic guidelines for drug development has yielded unprecedented results in the design and optimization of drug candidates against important diseases. By experimentally monitoring not only binding affinity but the thermodynamic forces that determine binding, enthalpy and entropy, it is possible to significantly accelerate the development process, as these forces reflect different types of drug-target interactions. A thermodynamic platform allows faster potency and selectivity optimization in conformity with existing rules for the design of drug-like molecules and oral bioavailability.

This overview presents the Structure-Based Drug Design, with a focus on what can be accomplished and its long-term potential. While database mining has repeatedly been able to identify compounds in the Available Chemical Directory (ACD) that inhibit enzymes and allosteric systems in the low micromolar range, it is only with extensive chemical efforts that nanomolar inhibitors have been developed. Merging combinatorial chemistry strategies with structure-based design principles has had a major impact on our ability to identify sub-nanomolar inhibitors with the selectivity and pharmaceutical properties to be plausible preclinical candidates.

Drug discovery relies on our ability to mine the chemical and biological data in an integrated manner. From the initial choice of chemical descriptors to the similarity coefficient itself, each user- and software-made decision will modify the perception of chemical similarity and influence the results of chemical space exploration. Such decisions will alter even the meaning of “similar”, leading to counter-intuitive neighbors. This effort is further complicated by the existence of chemical and target affinity cliffs, which is also noted in the area of marketed drugs. Indeed, similar molecules may have a different activity profile for the same array of properties, leading to a different clinical profile. Minor changes are sometimes responsible for the difference between “launched” and “withdrawn”. The “Black Swan”, a metaphor for the impact of the highly improbable, appears to be an inherent quality of the drug discovery enterprise. Predictions are based on the past, and can not substitute for proper design and use of experiments. Systems chemical biology may alleviate some of the inherent limitations related to prediction, by better integrating available experimental data.

Medicinal chemistry is a biology-based and chemistry driven discipline. One of the main drivers in medicinal chemistry is synthetic chemistry.

We have seen great contributions to the process of drug discovery by synthetic chemistry. One might recall the era of natural product chemistry and of combinatorial chemistry. Nevertheless, one who has followed the developments in medicinal chemistry over the last decades gets the impression that the discipline is pushing against a wall without really breaking through.

The real need for this breakthrough is highlighted by the attrition rates: for nearly one third of newly assessed targets we are not able to find leads and – even more disturbing – one third of the leads can not be optimalized into early clinical candidates.

The burning question is how to bridge the gap between the structure of a compound and its properties. Which compound should be tested and which should be made next? For decades, medicinal chemistry has been linked to these questions.

Recent efforts are directed towards the making of properties rather than the making of chemical structures. The physical chemical properties to be made are being described in such a way that they can be handled by chemists. These developments will be discussed. One of them is the renaissance of natural products. The unique chemical space occupied by natural products has led to a renewed interest in them.

Finally, we will touch briefly upon a major disconnection between academia and pharmaceutical industry. In these two domains medicinal chemistry and in particular synthesis seem to have a different meaning. Solving this disconnection might bring closer an answer to the burning questions in medicinal chemistry.

In an attempt to reduce the attrition rate of new chemical entities (NCEs) in development, many pharmaceutical companies have implemented a new drug discovery paradigm that involves optimizing the “pharmacological activity” as well as the “drug-like” properties (e.g. solubility, chemical/enzymatic stability, protein binding, cell permeability) during the lead optimization process in drug discovery. Traditionally, optimizing these “drug-like” properties was not considered by discovery scientists to be their responsibility. Instead, discovery scientists felt that undesirable “drug-like” properties of drug candidates would be “fixed” by preclinical development scientists. However, today most discovery scientists recognize that the “drug-like” properties of NCEs are intrinsic properties of the molecules and that it is their responsibility to optimize both the “pharmacological activity” and the “drug-like” properties of these molecules. In this chapter, the rational for this paradigm shift in drug discovery is discussed. In addition, a case history focusing on the optimization of the “drug-like” properties of cyclic prodrugs of an opioid peptide DADLE (H-Tyr-D-Ala-Gly-Phe-D-Leu-OH) is provided.

Hypothesis generation is an essential step in scientific discovery. It involves an analysis of existing evidence, the generation of a “theory” (or model) which leads to a hypothesis (about the unknown) tested by new experiments. The design of new drugs follows a similar scheme starting with an effective mining of a huge amount of collected experimental in vitro and in vivo data. These data often come from many different areas such as chemistry, biology, pharmacology, toxicology etc. and in various formats. Extracting the critical information is a challenging task that is performed by medicinal chemists and other scientists. Our goal is to assist the hypothesis generation and decision process by mimicking as much as possible the human reasoning. To do so, we have developed a decision-support system KEM (Knowledge Extraction and Management), based on the Galois lattices theory. KEM assists the scientist for efficiently generating and managing consistent hypothesis from experimental data. KEM® is a machine learning software that can guide multi-objective optimisation. We present here a few examples of data analysis by using KEM.

Since the approval of insulin in 1982 as the first recombinant protein, an impressive development has taken place. Meanwhile, more than 120 of recombinant drug substances have been approved and become available as extremely valuable therapeutic options. Based on a somewhat artificial but useful system one can classify recombinant drugs as first generation (changes due to technological concessions), second generation (authentic biomolecules), third generation (deliberately introduced modifications in order to improve mainly pharmacokinetic properties) and fourth generation drugs (newly invented, artificial proteins). Authenticity – exact copying of the most common human form – is not anymore a value per se, as challenges primarily related to the pharmacokinetics of artificial recombinant drugs can be overcome by diverging from the original. On the other hand, relatively minor changes in manufacturing or packaging may impact safety of therapeutic proteins.

Achieving effective oral delivery of therapeutic proteins means circumventing physiological barriers which seem to have evolved specifically to prevent this process. There are two classes of barriers, the intestinal wall and enzymatic degradation. Permeation constraints at the intestinal wall are attributed to the large molecular size of the proteins which prevent the paracellular route of permeation while their high aqueous solubility/hydrophilicity greatly constrains transcellular diffusion. The GI tract, on the other hand, contains a variety of proteases designed to degrade large proteins all the way to di-and tri-peptides by the time they reach the basolateral side of the epithelial cell layer of the small intestine.

Many approaches have been and are being investigated to address the challenge of oral protein delivery, only a subset of which falls in the category of nanotechnology. This article treats a number of these nanotechnologies including micro-machined devices delivering nanoliter volumes, mesoporous silica nanoparticles, protein nano-vessels and receptor-mediated endocytotic delivery of protein nanoparticles.

Based on the dictum: No acid – no ulcer a number of pharmaceutical companies started up research aiming at a drug, inhibiting gastric acid secretion during 1960s. The focus of that research was gastrin, the gastric acid stimulating hormone released from G-cells in the antral part of the stomach. The Searle company in the US tried to find a small molecule inhibiting the gastrin receptor, while AstraHässle in Sweden tried to find something that decreased the release of gastrin from the G-cells. SK&F focused on the H₂-receptor also involved in the regulation of gastric acid secretion. The SK&F approach lead to the first clinically useful acid inhibitor, cimetidine. The AstraHässle approach was a dead end, as was the Searle approach. However, the Searle idea led to the discovery of CMN 131, a compound which had some acid inhibitory effect, but was also toxic. When AstraHässle failed with their first attemptthey picked up on CMN 131 as a new lead around 1972. This approach resulted in timoprazole (1974), picoprazole (1977), and eventually omeprazole (1979). Omeprazole was first launched in Sweden 1988, under the trade name Losec. In 1997 Losec reached a yearly sale of more that 6 billion USD and became the world's biggest drug.

The AstraHässle project, which ended up with the approval of omeprazole 1988, started already 1967. Thus it took more than 20 years from the idea to market. Would that have been possible today with the present time-press and recourse restrictions within drug industry? It's doubtful. The struggle and issues to be solved during the journey are described.

Cannabinoids constitute an area of intensive research, both in industry and in academic institutions. Approximately 15 years ago, tremendous progress has been made in the molecular characterization of endogenous cannabinoids and their receptors. Cannabinoid CB₁ receptor antagonists showed clinical efficacy in the treatment of obesity and improved cardiovascular and metabolic risk factors. They have good prospects in other therapeutic areas, including smoking and alcohol addiction. Solvay's research achievements in the fast-moving field of CB₁ receptor antagonists are highlighted in relation with the general state of the art. Solvay pursued several medicinal chemistry strategies. The application of the concept of conformational constraint led to the discovery of rigidified analogs of the prototypic CB₁ receptor antagonist rimonabant. Modifications at the 4-position of the pyrazole ring in rimonabant led to a novel compound with retained CB₁ receptor antagonistic potency but with a different predicted biodegradation profile. Bioisosteric replacement of the central heterocyclic pyrazole ring in rimonabant yielded imidazoles, triazoles and thiazoles as selective CB₁ receptor antagonists. Dedicated medium throughput screening efforts delivered one diarylpyrazoline hit. This initial hit showed poor pharmacokinetic properties but could be successfully optimized into the orally active and highly CB₁/CB₂ receptor selective drug candidate ibipinabant, which is the subject of a development agreement between Solvay and Bristol-Myers Squibb.