User:Informagenesis

COMBINATORIAL LIBRARIES

During the past decade medicinal chemistry has been dramatically evolving [1]. A staggeringly huge amount of innovative knowledge and related information has jointly been accumulated in the field of modern drug design & development. As a result, three novel science disciplines were founded and comprehensively validated; these include: combinatorial chemistry, biological (in vitro and in vivo) as well as virtual (in silico) screening. These disciplines are intimately joined together by the whole idea to develop novel drug compounds following the optimal (rational), maximally effective and commercially viable strategy. Currently, modern drug design & development is based entirely on these disciplines.

The methods of rational drug design substantially affect all areas of drug discovery, including identification and validation of biotargets, highthroughput screening, combinatorial chemistry, in vitro compound profiling and clinical studies. The key step in the development pipeline is the initial bioscreening of targets with discovery compounds libraries. Its purpose is to identify the target-active compounds (hits) as a source for next development stage: secondary profiling and lead optimization. Although the larger pharmaceutical companies synthesize proprietary libraries in house, the chemistry companies specializing in combinatorial libraries became the major source of discovery compounds in the last several years. (Author's comments: this citation was taken from our own article: Konstantin V. Balakin, Pharma ex machina, Modern drug discovery, 2003, 45-47. I have a copyright form by K.V. Balakin, he is my colleague) In particular, combinatorial chemistry has increasingly emerged as a separate field within the scope of organic chemistry; a number of scientific publications have been published in various hard-copy journals and books. For instance, there are several fundamental works that describe the theoretical principles, methods, apparatus and schemes of combinatorial chemistry and parallel solid/liquid-phase synthesis [2].

COMBINATORIAL LIBRARY DESIGN

Combinatorial chemistry is primarily targeted for the synthesis of large number of organic molecules that compose compound libraries. These libraries can be further screened efficiently during a biological evaluation. This discipline is based wholly on organic synthesis methodologies resembling the classical drug design. The core difference lies in the number of compounds outputted; instead the single molecule produced by the traditional organic synthesis, the extensive number of compounds can be readily obtained using the combinatorial chemistry approaches. However, such quantitative growth is not considered to be quite sufficient to achieve a qualitative growth expressed in the number of active agents. Therefore, scientists have to use a straightforward route to find active compounds (lead compounds and drug candidates) within the scope of organic compound libraries. Thus, combinatorial synthesis is not a random procedure; in particular it represents a systematic and repetitive approach by which diverse molecules can be effectively obtained using "building blocks" as initial reactants. Each component from the targeted combinatorial library can be reasonably regarded as potential drug compound. Currently, several different combinatorial strategies have been developed based on the common core philosophy – targeted combinatorial synthesis instead of random. This way has lead to novel promising discipline named Focused/Targeted Library Design [3]. Using this strategy scientists significantly reduce the initial random chemical space to the separate virtual sets, that contain significantly lower number of structures. These structures are directly focused against single or multiplet biological targets using methods and algorithms of Virtual Screening methodology (for review, see ref [4]).

To date, most, if not all, pharmaceutical companies use combinatorial chemistry to create libraries of organic compounds which then are going to be evaluated in biological trials via high-throughput biological screening (HTS). Among them ChemDiv, Inc [5] is one of the key players within this field which possesses all the resources and tools to create the extended combinatorial libraries of high structural and target diversity. The tremendous scientific potential and research skills of ChemDiv staff provide a solid basis to solve a variety of tasks occurred widely in modern drug design & development. Thus, straight after the combinatorial stage has been successfully performed the resulting ten thousands individual molecules are thoroughly tested towards the activity selected (targeted) using modern, high-performance techniques and apparatus. Compounds are screened in a painstaking attempt to find drug-candidates and lead compounds that presumably will be able to interact with the selected target receptor in the human as well as lead optimization. Combinatorial synthesis can be performed in various ways, but all of the forms applied represent a complex interplay between classical organic synthesis techniques, rational drug design strategies, automated robotic tools, and scientific information management.

Combinatorial libraries are the direct outcome of combinatorial synthesis. The key principle of combinatorial synthesis is schematically depicted in Fig. 1. As shown in the figure presented, the initial building blocks include 20 starting reagents and 50 reactants. For example, primarily/secondary amines and carboxylic acid anhydrides can be regarded as effective building blocks for the creation of carboxamide combinatorial library. As a result of the first round of combinatorial synthesis 1,000 final products can be obtained using a particular combinatorial technique. The formed compounds which are contained the additional point of diversity, for example carboxylic function, can be further reacted with the collection of 50 reactants providing the final combinatorial library consisted of 50,000 second generation products. In particular, using such reactions promising peptidomimetic compounds can be readily obtained. Representative examples of some combinatorial schemes recently published in highly specialized journals, like the Journal of Combinatorial Chemistry, are shown in Fig. 2.



Fig. 1. Pyramid technique for producing large combinatorial chemical libraries (Author's comments: this picture was originally created by Richard Twyman and published in http://genome.wellcome.ac.uk/doc_WTD021035.html. I have modified the picture. It should be additionally noted that “Pyramid technique” image is a common strategy usually depicted as a pyramid)



Fig. 2. Examples of combinatorial schemes published by (A) A.Ivachtchenko et al [6]; (B) A. Trifilenkov and colleagues [7]; (C) I. Dalinger [8]; (D) A.Ivachtchenko et al [9]; and (E) A. Ilyn [10] (Author's comments: all the examples performed below were taken from our own articles published in various journals, see ref section. This picture does not need any permission!)

The appropriate strategy for the design of combinatorial libraries is developed in accordance with the target, disease area, resources on hand and specific project goals. In general sense, the combinatorial synthesis can be defined as the process of production of all possible combinations of appropriate reagents using a given reaction. Obviously, the number of possible reaction products greatly exceeds the synthetic and screening resources even at the largest pharmaceutical companies. For example, let’s consider the reaction of amide condensation using the available reagents from the ChemDiv, Inc current catalogue of building blocks. One can select 4079 primary amine synthons and 3375 carboxylic acid synthons. Combining these two reagent sets would result in a total of over 13 million products. Both the production and the screening of such a library would be economically unreasonable. In addition, the ability of modern combinatorial synthetic methods to effectively provide large numbers of compounds does not ensure that screening collections generated by this means make the most effective use of HTS resources. Therefore, a problem of constraining the size of such virtual combinatorial libraries appears. This problem is solved via rational combinatorial library design aiming in selection of a “rational” compound subset that would meet the predefined goals of a bioscreening program. Moreover, to maximize the opportunity for lead generation from a given compound collection, the constituents of the collection should be as diverse as possible. There are many examples from literature in which combinatorial library synthesis successfully complemented structure-based design techniques in drug discovery [11].

Currently, there are several synthetic strategies and methods commonly applied in modern combinatorial chemistry. These include the Parke-Davis Pharmaceutical DIVERSOMER and Affymax VLSIPS techniques, methods designed to obtain a series of compound mixtures as well as “split and mix” approach.

Besides several combinatorial techniques just listed a number of famous organic reactions (methods) have been successfully adapted for combinatorial format. For example, many multicomponent reactions (MCRs) are quite compatible with high-throughput combinatorial synthesis. MCRs can be carried out very efficiently in solution and are suitable for the synthesis of libraries of diverse small molecules. A classical example is the four-component Ugi reaction between aldehyde, amine, isonitrile and carboxylic acid, which has emerged as a powerful tool for rapid identification and optimization of lead compounds in drug discovery (for example, see Fig.2(E)) [12].

Finally, in contrast to the tools, you may see in the laboratory of classical organic synthesis, combinatorial synthesis is routinely performed using advanced automated devices which are specifically adapted to produce numerous compounds simultaneously. Among such apparatus automated and semi-automated combinatorial reactors are the most effective tools for combinatorial library synthesis; these, in particular, include: Microsynth system, a multimode microwave that can accommodate reaction volumes up to one liter (Milestone Inc. [13]), Microfluidic device for combinatorial chemistry by Sowmya Kondapalli and colleagues [14], Synthesis-1 (Heidolph UK [15]), Combinatorial reactor developed by Bioorganic Chemistry group (BOC), Semi-automatic synthesis system (SAS) developed by Multisyntech GmbH [17], Laboratory synthesizer CombiSyn-012-3000 developed by ChemDiv, Inc [5]. For example, CombiSyn-012-300012 was developed by ChemDiv, Inc for the preparation of numerous organic compounds in an effective combinatorial way. This semi-automated device provides some advanced opportunities for high-throughput solution-phase combinatorial synthesis. Workup, isolation, purification, and analytical procedures can be carried out using this technology platform, which includes all the equipment required for parallel synthesis of large combinatorial libraries. ChemDiv is a global discovery and development organization headquartered in San Diego, CA, USA with subsidiaries in Russia, business and logistics operations around the world. ChemDiv is a provider of integrated discovery and development solutions based on a strong combinatorial chemistry platform. Integrated Discovery outSource™ solutions offered by ChemDiv cover the complete range of disciplines needed to bring a project in CNS, oncology, inflammation, metabolic and infectious disease area from identification of a biological target (protein expression, assay development etc.) to clinical drug candidates (ADME/DMPK, toxicity and safety studies, efficacy models etc.) to Proof of Concept drug candidate (Phase I and II).

REFERENCES

1. (a) Gareth Thomas. Medicinal Chemistry: An Introduction, Wiley, 2001, 568 pp.; (b) Burger’s Medicinal Chemistry and Drug Discovery, 6th ed.,Vol. 1: Drug Discovery, John Wiley & Sons, 2003, 932 pp.; (c) P. Bultinck, et al., ed. Computational Medicinal Chemistry for Drug Discovery. Marcel Dekker, 2004, 806 pp.; (d) Denora N, Trapani A, Laquintana V, Lopedota A, Trapani G. Recent advances in medicinal chemistry and pharmaceutical technology--strategies for drug delivery to the brain. Curr. Top Med. Chem., 2009; 9(2): 182-96; (e) Morphy R, Rankovic Z. Designing multiple ligands - medicinal chemistry strategies and challenges. Curr. Pharm. Des., 2009; 15(6): 587-600; (f) Gwaltney SL 2nd. Medicinal chemistry approaches to the inhibition of dipeptidyl peptidase IV. Curr. Top Med. Chem., 2008; 8(17): 1545-52; (g) Sawa M. Strategies for the design of selective protein kinase inhibitors. Mini Rev. Med. Chem., 2008 Oct; 8(12): 1291-7; (h) Yu F, Liu X, Zhan P, De Clercq E. Recent advances in the research of HIV-1 RNase H inhibitors. Mini Rev. Med. Chem. 2008; 8(12) :1243-51.

2. (a) Nicolaou, K. C., Hanko, Rudolf, Hartwig, Wolfgang (eds.) Handbook of Combinatorial Chemistry, 2002, Wiley-VCH, Weinheim; (b) G. Jung (ed.) (1999) Combinatorial Chemistry - Synthesis, Analysis, Screening (580 pages), Wiley-VCH, Weinheim; (c) Gordon, Eric M. / Kerwin, James F. (eds.), Combinatorial Chemistry and Molecular Diversity in Drug Discovery, 1998, John Wiley & Sons; (d) Beck-Sickinger, Annette / Weber, Peter, Combinatorial Strategies in Biology and Chemistry, 2001, John Wiley & Sons; (e) Seneci, Pierfausto, Solid-Phase Synthesis and Combinatorial Technologies, 2000, John Wiley & Sons; (f) Ehrfeld, Wolfgang / Hessel, Volker / Löwe, Holger, Microreactors, 2000, Wiley-VCH, Weinheim.

3. (a) N.P. Savchuk, S.E. Tkachenko, K.V. Balakin, et al. Rational Design of GPCR-specific Combinational Libraries Based on Concept of Privileged Substructures / In Chemoinformatics in Drug Discovery. Ed. Tudor I. Oprea, Vol 23, Wiley-VCH, 2005, 287-313; (b) N.P. Savchuk, S.E. Tkachenko, K.V. Balakin. Rational Design of GPCR-specific Combinational Libraries Based on the Concept of Privileged Substructures / In Chemoinformatics in Drug Discovery, Ed. Prof. Dr. Tudor I. Oprea, Wiley-VCH Verlag GmbH & Co. KGaA, 2005; (c) K.V. Balakin, Y.A. Ivanenkov, et al. Synergy of Advanced Virtual and High-Throughput Screening Technologies for Increasing Productivity of Small Molecule Drug Discovery / In New Research on Biotechnology in Biology and Medicine. Eds. Egorov A.M., Zaikov G. Chapter 13: pp. 123-131. Nova Science Publishers, 2006; (d) K.V. Balakin, et al. Computer Algorithms for Selecting Molecule Libraries for Synthesis / In Computer Applications in Pharmaceutical Research and Development. Ed. Sean Ekins, John Wiley & Sons, Inc. 2006; (e) A.M. Aronov, K.V. Balakin, A. Kiselyov, S. Varma-O'Brien and S. Ekins. Applications of QSAR Methods to Ion Channels / In Computational Toxicology: Risk Assessment for Pharmaceutical and Enviromental Chemicals, Ed. Sean Ekins, 2007, John Wiley & Sons, Inc.; (f) S. Ekins, et al. Applications of QSAR to Enzymes Involved in Toxicology/ In Computational Toxicology: Risk Assessment for Pharmaceutical and Enviromental Chemicals, Ed. Sean Ekins, 2007, John Wiley & Sons, Inc.; (g) A. Ivachtchenko, I. Okun, S. Tkachenko, A. Kiselyov, Y. Ivanenkov and K. Balakin. Non-Peptide Small Molecule Inhibitors of Caspases / In Design of Caspase Inhibitors as Potential Clinical Agents, Eds. Tom O’Brien and Steve Linton, Taylor & Francis Group, 2007, Volume: 5; (h) Y. A. Ivanenkov and L. M. Khandarova. Advanced methods of artificial intelligence in the design of pharmaceutical agents / In Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery, Ed. K.V. Balakin M.Sc., Ph.D., D.Sc., John Wiley & Sons, Inc. 2009 [ahead to print]; (i) I. Pletnev, Y. Ivanenkov, and A. Tarasov. Data mining algorithms for pharmaceutical discovery / In Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery, Ed. K.V. Balakin M.Sc., Ph.D., D.Sc., John Wiley & Sons, Inc. 2009 [ahead to print]; (j) K. V. Balakin, Y. A. Ivanenkov, et al. Compound Library Design for Target Families / In Chemogenomics: Methods and Applications, Ed. E. Jacoby 2009, Ph.D., D.Sc., Humana Press (Springer), 2009 [ahead to print].

4. (a) Ivanenkov Y.A., Balakin K.V., Skorenko A.V., Tkachenko S.E., Savchuk N.P., Ivachtchenko A.A., Nikolsky Y. Application of advanced machine learning algorithm for profiling specific GPCR-active compounds. Chem. Today., 2003, 21, 72-75; (b) Nikolsky Y., Balakin K.V., Ivanenkov Y.A., Ivachtchenko A.A., Savchuk N.P. Intelligent machine learning technologies in pre-synthetic combinatorial design. PharmaChem., 2003, 4, 68-72; (c) Ivanenkov Y.A., Balakin K.V., Savchuk N.P., Ivachtchenko A.A., Skorenko A.V., Nikolsky Y. Advanced Data Mining Tools for Compounds Libraries. European Biotechnology News., 2003, 2, 40-41; (d) Balakin KV, Kozintsev AV, Kiselyov AS, Savchuk NP. Rational design approaches to chemical libraries for hit identification. Curr Drug Discov Technol. 2006 Mar;3(1):49-65; (e) K. V. Balakin, Y. A. Ivanenkov, N. P. Savchuk, A. A. Ivachtchenko, S. Ekins. Comprehensive computational assessment of ADME properties using mapping techniques. Curr. Drug Disc. Techn., 2005, 2, 99-113; (f) Tkachenko SE, Okun I, Balakin KV, Petersen CE, Ivanenkov YA, Savchuk NP, Ivashchenko AA. Efficient optimization strategy for marginal hits active against abl tyrosine kinases. Curr Drug Discov Technol. 2004 Oct;1(3):201-10; (g) Savchuk NP, Balakin KV, Tkachenko SE. Exploring the chemogenomic knowledge space with annotated chemical libraries. Savchuk NP, Balakin KV, Tkachenko SE; (h) Balakin KV, Ekins S, Bugrim A, Ivanenkov YA, Korolev D, Nikolsky YV, Skorenko AV, Ivashchenko AA, Savchuk NP, Nikolskaya T. Kohonen maps for prediction of binding to human cytochrome P450 3A4. Drug Metab Dispos. 2004 Oct;32(10):1183-9; (i) Balakin KV, Lang SA, Skorenko AV, Tkachenko SE, Ivashchenko AA, Savchuk NP. Structure-based versus property-based approaches in the design of G-protein-coupled receptor-targeted libraries. J Chem Inf Comput Sci. 2003 Sep-Oct;43(5):1553-62; (j) Lang SA, Kozyukov AV, Balakin KV, Skorenko AV, Ivashchenko AA, Savchuk NP. Classification scheme for the design of serine protease targeted compound libraries, J Comput Aided Mol Des. 2002 Nov;16(11):803-7; (k) Balakin KV, Tkachenko SE, Lang SA, Okun I, Ivashchenko AA, Savchuk NP. Property-based design of GPCR-targeted library. J Chem Inf Comput Sci. 2002 Nov-Dec;42(6):1332-42.

5. http://www.chemdiv.com

6. Alexandre V. Ivachtchenko, Vladimir V. Kobak, Alexey P. Ilyn, Alexander V. Khvat, Volodymir M. Kysil, Caroline T. Williams, Julia A. Kuzovkova, and Dmitry V. Kravchenko. An Efficient, Parallel Liquid-Phase Synthesis of N-Substituted 6-Aminosulfonyl-2-Oxo-1,2-Dihydroquinoline-4-Carboxamide and 6-Aminosulfonylquinoline-4-Carboxamide Derivatives, J. Comb. Chem. 2005, 7(2), 227-35.

7. Andrey S. Trifilenkov, Vladimir V. Kobak, Marina A. Salina, Julya A. Kusovkova, Alexey P. Ilyin, Alexander V. Khvat, Sergey E. Tkachenko and Alexandre V. Ivachtchenko. Liquid-Phase Parallel Synthesis of Combinatorial Libraries of Substituted 6-Carbamoyl-3,4-dihydro-2H-benzo[1,4]thiazines, J. Comb. Chem., 2006, 8 (4), pp 469–479.

8. Igor L. Dalinger, Irina A. Vatsadse, Svyatoslav A. Shevelev, and Alexandre V. Ivachtchenko. Liquid-Phase Synthesis of Combinatorial Libraries Based on 7-Trifluoromethyl-Substituted Pyrazolo[1,5-a]Pyrimidine Scaffold, J. Comb. Chem., 2005, 7 (2), pp 236–245.

9. Alexandre Ivachtchenko, Sergiy Kovalenko, Olena V. Tkachenko, and Oleksiy Parkhomenko. Synthesis of Substituted Thienopyrimidine-4-ones, J. Comb. Chem., 2004, 6 (4), pp 573–583.

10. Alexey P. Ilyn, Andrey S. Trifilenkov, Sergey A. Tsirulnikov, Irina D. Kurashvily, and Alexandre V. Ivachtchenko. Synthesis of 4-Oxo-4,5,6,7-tetrahydropyrazolo[1,5-a]pyrazine-6-carboxamides using a modification of Ugi condensation, J Comb Chem. 2005 Nov-Dec;7(6):806-8.

11. Tondi D, Costi MP. Enhancing the drug discovery process by integration of structure-based design and combinatorial synthesis. In: Viswanadhan AK, Ghose VN, editors. Combinatorial library design and evaluation. New York: Marcel Dekker Inc, 2001. p. 563-604.

12. Domling, and I. Ugi. Multicomponent reactions with isocyanides. Angew Chem 2000; 112: 3300–3344.

13. http://www.milestonesci.com

14. Sowmya Kondapalli, David A. Putnam, and Brian J. Kirby. Microfluidic Device For Combinatorial Chemistry, In the 2007 Annual Meeting Salt Lake City, UT, November 6, 2007, 214c.

15. http://www.heidolph-uk.co.uk

16. http://wzar.unizar.es/acad/fac/cie/quiorg/asimetrica/mtm.html

17. http://www.multisyntech.com