Download Quantum Molecular Design of Drugs

WHITE PAPER
Quantum Molecular Design of Drugs
Page
Cloud Pharmaceuticals, Inc.
6 David Drive
Research Triangle Park, NC
www.cloudpharmaceuticals.com
1.919.424.6894
Email: [email protected]
1
An In Silico Approach to Drug Discovery and
Design in Novel Molecular Space
© 2015 Cloud Pharmaceuticals, Inc.
Contents
Searching Virtual “Chemical Space” ............................................................................................................ 3
Exploiting QM/MM ..................................................................................................................................... 4
Jak 3 Case Study Accurately Predicting Ligand Binding ............................................................................... 5
Quantum Molecular Design Offering .......................................................................................................... 9
Page
2
Partnering ................................................................................................................................................... 9
© 2015 Cloud Pharmaceuticals, Inc.
Searching Virtual “Chemical Space”
Chemical space is vast, with an estimated
1065 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. 2006.
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.
Page
3
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.
© 2015 Cloud Pharmaceuticals, Inc.
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.
Page
4
Cloud Pharmaceuticals enables the efficient use of QM/MM by using it in tandem with the highly
efficient Quantum Molecular Design search algorithm.
© 2015 Cloud Pharmaceuticals, Inc.
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, IL10, 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.
Page
5
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.
© 2015 Cloud Pharmaceuticals, Inc.
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
Page
6
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.
© 2015 Cloud Pharmaceuticals, Inc.
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 0.77. The determined
coefficients for our energy term then
reproduce the test set data with a correlation
of 0.75.
Page
7
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.
© 2015 Cloud Pharmaceuticals, Inc.
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
Page
8
Quantum Molecular Design
© 2015 Cloud Pharmaceuticals, Inc.
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
© 2015 Cloud Pharmaceuticals, Inc.
Page
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
[email protected].
9
Partnering