Quantum Molecular Design of Drugs

Transcription

1 Page1 W H I T E P A P E R Quantum Molecular Design of Drugs An In Silico Approach to Drug Discovery and Design in Novel Molecular Space Cloud Pharmaceuticals, Inc. 6 David Drive Research Triangle Park, NC info@cloudpharmaceuticals.com

2 Page2 Contents Searching Virtual Chemical Space... 3 Exploiting QM/MM... 4 Jak 3 Case Study Accurately Predicting Ligand Binding... 5 Quantum Molecular Design Offering... 9 Partnering... 9

3 Page3 Searching Virtual Chemical Space Chemical space is vast, with an estimated stable molecules accessible with molecular weights below 850. Designing new drugs that bind to a specified protein target requires finding the best molecule in this vast chemical space. Exploration of this space by direct enumeration and evaluation is prohibitively costly. Cost effective searching requires employing optimization techniques. Our novel "Quantum Molecular Design" method can search large chemical space much more efficiently. Quantum Molecular Design uses "reverse engineering" methods to solve the problem of going from a set of desired properties back to realistic chemical structures and material morphologies that may have these properties. The specific implementation used by Cloud Pharmaceuticals is based on the pioneering work developed in Duke University, as shown in the figure taken from Wang et al, J. Am. Chem. Soc The Beratan and Yang groups at Duke University have developed the linear combination of atomic potentials (LCAP) approach, using molecular characteristics as a function of parameters that define the contribution of a specific chemical group at a particular chemical site in a molecule. This method enables the construction of a potentially enormous virtual library of chemical structures at a cost far below the factorial cost of individual structure evaluation. The LCAP method has several advantages, such as multiple search methods and ease of parallelization, and has been published and experimentally tested. Cloud Pharmaceuticals has improved and enhanced the Quantum Molecular Design algorithm to allow versatile and multiple chemical groups and has added a novel implementation that is based on an integer programming method. This has allowed us to implement and reap the benefits of the algorithm in previously unused areas, especially computational drug design.

4 Page4 Exploiting QM/MM The process of drug discovery involves the identification of molecular candidates, synthesis, characterization, screening, and assays for therapeutic efficacy. It is generally recognized that drug discovery and development are very time and resource consuming processes. Computer-Aided Drug Design (CADD) is a specialized subdiscipline of rational drug design that uses computational methods to simulate drugreceptor interactions and can save time and money. Virtual high throughput in silico screening of ligand binding can significantly reduce the time required for lead discovery and lead optimization. One of the most common tools of in silico binding analysis is the use of docking algorithms to rapidly predict relative binding affinities of a large number of ligands for a given protein. If there is a hit with a particular ligand, it can be extracted from the database for further testing. This molecule can then go through ADMET (Adsorption, Distribution, Metabolism, Elimination and Toxicity) evaluation, as well as synthesis, biological activity and refinement in order to generate a drug. However, there is a major problem with how the drug design industry uses docking tools. While docking tools are cheap in computer time and allow fast scans of large libraries, the accuracy of calculating the binding strength (of ligands to therapeutic targets such as protein) is very poor and the results are not predictive of the experimental data. There are much more accurate methods than those currently used by the industry, but the cost (in computer time) of such exhaustive calculations is very expensive, especially if one has to scan a very large library of molecules. QM/MM methods are multi-scale/multi-resolution computational methods to calculate ligand binding. Using a combination of quantum chemistry (QM) tools to characterize the ligand, and molecular mechanics (MM) tools to describe the protein and solvent we obtain a deeper understanding of protein-ligand interactions, which is also more accurate. The method includes flexibility of the both the protein and ligand, as well as explicit water description. Cloud Pharmaceuticals enables the efficient use of QM/MM by using it in tandem with the highly efficient Quantum Molecular Design search algorithm.

5 Page5 Jak 3 Case Study Accurately Predicting Ligand Binding The successful treatment of diseases is highly dependent on the availability of effective medication, often consisting of small molecules. Calculating the properties of all molecules in a large chemical library can be time consuming and cost prohibitive. Cloud Pharmaceuticals solves this problem by using the Quantum Molecular Design search algorithm to scan large libraries to find the strongest inhibitors of a specific biological target and then calculate their binding strength using high accuracy QM/MM calculations. We applied this methodology to the Janus family of kinases in order to discover novel inhibitors of the JAK3 enzyme. The Janus family of kinases includes JAK1, JAK2, JAK3, and TYK2. The JAK family is an active target for the development of drugs for Rheumatoid Arthritis, immunosuppression and inflammation due to their interaction with cytokine receptors. Cytokine receptors are instrumental in modulating the immune system and are responsible for balancing humoral and cell-based immune responses and regulating the maturation, growth and responsiveness of vital immune cell populations. However, these receptors have no enzymatic activity and rely completely on the JAK enzymes to initiate signaling. Inhibitors targeting JAK3 that do not target other members of the family, can produce more focused results with fewer side effects. JAK3 is involved in signaling IL-2 (T cell development), IL-4 (Th2 cell differentiation), IL-7 (thymocyte development), IL-9 (hematopoietic cells), IL-15 (NK cell development), and IL-21 (immunoglobulin class switching). JAK1 is involved in signaling all of these except IL-21 and also is part of the signaling mechanism for IL-6, IL-10, IL-11, LIF, OSM, CT-1, CTNF, NNT-1, Leptin, and both type 1 and type2 interferon. JAK2 has been associated with the signaling of IL-3, IL-5, IL-6, and interferon as well as single chain receptors (e.g. Epo-R, Tpo-R, GH-R, PRL-R). TYK2 is implicated in the signaling of interferon, IL-6, IL- 10, IL-11, IL-12, IL-27, IL-31, OSM, ciliary neurotrophic factor, cardiotrophin 1, cardiotrophin-like cytokine, and LIF. Since JAK3 s signaling is limited to interleukins, targeting JAK3 allows for creating drugs that potentiate interleukins without the unnecessary side effects causes by interruption of these other receptor types. Based on methodology developed at Duke University, Quantum Molecular Design is an innovative new method that transforms the discrete chemical space to a continuous one, allows efficient searching of that space, and cuts down the number of calculations required to locate promising lead structures in the library. Quantum Molecular Design uses "reverse engineering" methods to solve the problem of going from a set of desired properties back to realistic chemical structures and material morphologies that may have these properties. This methodology also has the advantage of parallelizability. Computational costs scale favorably with system size allowing for the use of highly parallel, very efficient computation machines. This permits the use of the accurate, but computationally costly, quantum mechanics/molecular mechanics (QM/MM) calculations resulting in very high accuracy binding affinity assays of a ligand to a protein.

6 Page6 Predicting binding affinities between a protein and a ligand is critical in order to rationally design new drugs. Ligand binding is dominated by two terms: energy and entropy. In order to calculate the energy and entropy that occurs during binding, the correct binding mode has to be known. The current available solutions are divided broadly into two kinds: Docking based methods (which are extremely fast, but not accurate, due to limitation of scoring functions) Free energy calculation methods, usually with thermodynamic integration (highly accurate, but very time consuming) Parameterization Process of Cloud Pharmaceuticals Cloud Pharmaceuticals methodology gets the correct binding mode and binding energy (entropy) using different ligand geometries in QM/MM energy calculations. Then we evaluate free energy (entropy) contributions with surface area and solvation terms. Using multiple linear regressions, we calculate the contribution of each term (binding energy, surface area and free-energy of solvation) for the best prediction of ligand binding (for example, measured IC50). This equation can now be used to predict what will be the binding strength of other ligands.

7 Page7 For each target protein, X-ray structure is validated and corrected and then stripped of its X-ray ligand and placed in a water box. After solvation, the waters and ions are equilibrated. Two ligand sets are prepared for parameterization, a training set and a test set. The ligand binding sets are chosen based on criteria of similarity to X-ray ligands, shared common core scaffold, diversity between the two data sets, and the size of the data set. The conformers for each ligand in the data sets are generated, the solvation term of the binding set is calculated, the solvent accessibility term is generated, and the QM/MM energy term is calculated. These steps are performed for all of the conformers of all of the ligand in the two data sets, which for Jak3 resulted in 3080 QM/MM parameterization runs, each one over 12 hours. A linear regression is performed to get the best coefficients for each of the binding energy terms to reproduce the experimental IC50. This figure shows that QM/MM calculations can predict, with high accuracy, the binding of a set of JAK3 inhibitors. The top panel shows the QM/MM calculation for a training set of 14 inhibitors (taken from Wang et al. Bioorg. Med. Chem. Lett. 18 (2008) p. 4907), while the bottom panel shows the same algorithm used for a test set (taken from Antczak et al. Bioorg. Med. Chem. Lett. 19 (2009) p. 6872). Using the training data set we were able to fit our results with a correlation of The determined coefficients for our energy term then reproduce the test set data with a correlation of The parameterized binding equation is used in the Quantum Molecular Design methodology. For finding novel Jak3 inhibitors, two virtual libraries of compounds were built, a smaller library built from a previously published scaffold containing mostly unpublished compounds and a larger library using a novel scaffold, keeping the major ligand-protein interactions from multiple available x-rays structures. The smaller library can generate approximately 1 million compounds by modifying six different functional groups, whereas the larger library has seven different functional groups and can generate approximately 36 million compounds.

8 Page8 The libraries are then used in the Quantum Molecular Design algorithm, starting from multiple random places in the library chemical space. The algorithm then goes through the library, improving the optimized property. QM/MM calculations are performed on all of the conformers from the ligands chosen by the algorithm. The parameterized binding equation is used to get a binding score that will determine the direction along the property surface. In total, five runs with different initial starting ligands were performed using the libraries. The top ligands with the strongest binding score were chosen. After the top ligands are chosen they are run through a series of filters to determine drug synthesizability, ADMET properties and intellectual property filters. We also tested the same model on sets of JAK1, JAK2, and TYK2 inhibitors. These result were then used to isolate molecules that precisely target JAK3 avoiding interaction with the rest of the JAK family of enzymes. These ligands are available for review after signing NDA. To summarize, we have successfully applied QM/MM computational methods to accurately predict the measured strength of binding of a ligand to a protein, thus enabling the use of computers to identify targeted inhibitors for JAK3, as well as numerous other pharmaceutical targets. Computational drug design methods enable researchers to reduce the time and costs of the drug discovery process by predicting experimental data in silico. Quantum Mechanics/Molecular Mechanics calculations are not normally used in drug design because such models are computationally extensive, although (as we have shown here) these methods offer much better accuracy than more commonly known computational methods. We have reduced the computation burden by combining QM/MM with Quantum Molecular Design (the search algorithm) tailored to high performance computation providing a computational speed and cost advantage over other methods of drug discovery. Drug Discovery Search Engine and Database Drug Target Protein Database Binding Database Process Structures and Binding Data Quantum Molecular Design

9 Page9 Quantum Molecular Design Offering Cloud Pharmaceuticals offers its Quantum Molecular Design process as a service via Microsoft Azure, Amazon EC2 and private cloud implementations. In a typical engagement, the customer provides a target and its X-ray structure, and Cloud Pharmaceuticals analyzes the target using Quantum Molecular Design, along with a number of bioinformatics filters to eliminate toxic leads and/or leads with poor manufacturing properties. An analysis begins with the design of a scaffold for a small molecule or a peptide, based on client choice. A calibration of the model is made validated by published or known data or assays conducted by our experimental partners. The depth of search of molecular space is determined by the customer s budget. Upon completion of a customer engagement, typically a six month effort for a single target, Cloud Pharmaceuticals provides a small, highly focused library of novel lead compounds or peptide drug candidates for each target. Quantum Molecular Design Workflow Partnering Cloud Pharmaceuticals partners with other biotechnology firms, pharmaceutical companies, medical research institutions, and government laboratories to further develop leads in our pipeline. We have applied Quantum Molecular Design to cancer, inflammation, autoimmune diseases, CNS indications and rare diseases. For further information, contact our business development team, or write to info@cloudpharmaceuticals.com.