Computational analysis of novel drug opportunities (CANDO)
The comprehensive solution to
characterise and treat every disease.
We developed the Computational Analysis of
Novel Drug Repurposing Opportunities (CANDO) platform for multiscale
therapeutic discovery, initially funded by a NIH Director’s Pioneer
Award, to overcome the limitations of traditional single
target/disease approaches. The platform screens and ranks
drugs/compounds for every disease/indication by considering their
interactions to every biological protein target, as well as
integration of extensive, multiscale data, from comprehensive
well-curated libraries. The interactions are determined using multiple
fast bioanalytic docking and hierarchical fragment-based docking with
dynamics protocols, among others. The platform continually uses
machine learning protocols to iterate on new relevant data from
preclinical and clinical studies and improve performance. CANDO not
only enables discovery of drugs tailored holistically to their
relevant environments but also provides insight into disease system
etiologies and mechanisms via analytics of drug–protein interactions
at multiple scales.
The CANDO platform represents a comprehensive integration of our group's applied research
on therapeutic discovery, building upon basic protein and proteome
structure, function, interaction, evolution, and design research.
Funding sources include the National Institutes Health (specifically a
2010 NIH Director's
Pioneer Award as well an NIH NCATS ASPIRE Design Challegen Award),
the National Science Foundation, the Kinship Foundation, the
University at Buffalo, the University of Washington Technology Gap
Innovation Fund, the Veteran's Health Administration, and the
Washington Research Foundation.
Key publications and sources of
information to understand CANDO past and present:
- Mangione W, Falls Z, Chopra G, Samudrala R. cando.py:
Open source software for predictive bioanalytics of large scale
drug-protein-disease data. Journal of Chemical Information
and Modeling 60: 4131-4136, 2020. [v2 application note.]
- Minie M, Chopra G, Sethi G, Horst JA, White G, Roy A, Hatti K,
Samudrala R. CANDO and
the infinite drug discovery frontier. Drug Discovery
Today 19: 1353-1363, 2014. [v1 capstone/overview.]
- Essay and
abstract
from the application for the 2010 NIH Director's Pioneer
Award.
- CANDO on GitHub
We are currently working with almost 30 collaborators throughout
the world to find cures for over 20 indications/diseases. See a full
list of our indications and
collaborators and some results in progress.
Drug discovery is protein folding with a compound.
This section is in progress. There's a lot of novelty to
this project, technically in terms of the methods used, and also in
terms of philosophy and paradigms employed (ergo, the reason for the
2010 NIH
Director's Pioneer Award). Here are a few of them:
Technical
- Docking with protein structure + ligand dynamics.
- Multitargeting.
- Automated binding site identification.
- Can be used to computationally assess new compounds from combinations of fragments (+).
- Using all the known information about current drug and drug like compounds.
- Learning from affinity measures separating entropy and enthalpy.
- Predict toxicity through nonspecific binding.
- Predict ligand-target networks.
- Fragmentation of drugs to identify pharmacophore.
- Drug comparisons to substrates and metabolites to find NCEs in the structural context of the binding site
- AI-based therapeutic discovery.
Conceptual/philosophical
- Drug profiles across multiple targets (not single drug per target paradigm).
- Molecular and systems level integration because of drug profile (i.e., how each compound interacts with the interactome).
- Exploiting the fact that all drug discovery thusfar has been a feature of Evolution.
- Consolidates almost all one off inhibitor discovery in one shotgun approach.
- Systems based drug discovery.
- New compounds (+) predicted to be nontoxic can be used to explore beyond the CANDO space for very intractable diseases.
- Can be used to create a system of existing and novel small molecules to manipulate living (and nonliving) systems
- If successful, it will move compbio frameworks forward unlike never before.
Personalisation
Ultimately the goal is personalisation to improve quality of life,
including personalised medicine. When I first came across genetics, my
dream was that each person would have their genome sequence and a
powerful computing cluster (these days, one can get a
personal supercomputer for ~$6000) where they could evaluate the
response of their proteins and proteomes (corresponding to their
specific genes and genomes) against entities in the environment, such
as bioactive chemical compounds, to improve their quality of life,
i.e., to treat and/or cure diseases as well develop vaccines. This
project is part of that dream and we're going to rigourously evaluate
whether it can come to fruition.
Everyone has a major responsibility, with some overlap. The rest of
our group also helps.
Current
- Ram Samudrala - PIon.
- Gaurav Chopra - fragment based docking with dynamics, shotgun systems and synthetic biology, operations.
- Liana Bruggemann - precision medicine and oncology.
- Manoj Mammen - application and validation.
- Matt Hudson - machine learning based virtual screening.
- Skyler Resendez - evolutionary informatics.
- Will Mangione - machine learning based shotgun drug discovery and repurposing.
- Zack Falls - shotgun drug discovery and repurposing, precision medicine.
Past
- Ambrish Roy - in virtuale bioininformatic docking pipeline.
- Andrew Ho - personalisation, individualised webbot.
- Brian Buttrick - function prediction for docking site identification.
- Brady Bernard - all around consultant, 3dtherapeutics, commercialisation.
- Brian Buttrick - in virtuale bioinformatic docking pipeline, network comparisons.
- David Beck - all around consultant.
- Ekachai Jenwitheesuk - initial development of early ideas, v0.
- Geetika Sethi - pipeline management, benchmarking.
- George White - collaborations, verifications, all rounder.
- Janez Konc - fragment based docking with dynamics.
- Jeremy Horst - original developer, all around consultant.
- Jeremy Li - personalisation, individualised webbot.
- Jim Schuler - interactome modelling, precision medicine.
- Kaushik Hatti - web application design.
- Ling-Hong Hung - shotgun structural and functional biology.
- Mark Minie - writing, all rounder.
- Haychoi Taing - systems and database administrator/programmer.
- Michael Shannon - former systems administrator.
- Thomas Wood - shotgun systems and synthetic biology.
Funding
(parts large and small)
- University at Buffalo Accelerator Fund (2021).
- University at CTSA pilot (2020-2021).
- US NSF SBIR subcontract from Onai, Inc. (2020).
- US NIH NCATS ASPIRE Design Challenge Award (2020-2022).
- US VHA Big Data Scientist Training Enhancement Program (2016-2018).
- US NIH Buffalo Research Innovation in Genomic and Healthcare Technology (BRIGHT) Education Award T15LM012495 (2016-2021).
- US NIH Clinical and Translational Sciences Award UL1TR001412 (2015-2020).
- US NIH Director's Pioneer Award 7DP1OD006779 (2010-2017).
- US NSF GEMSEC (2005-2011).
- US NSF CAREER Award IIS-0448502 (2005-2010).
- US NIH F30DE017522 (2006-2010).
- The University of Washington's Technology Gap Innovation Fund (2006-2007).
- Washington Research Foundation (2006-2007).
- Puget Sound Partners in Global Health (2004-2005).
- US NIH R33 (2003-2006)
- Searle Scholar Award to Ram Samudrala (2002-2005).
- The University of Washington's Advanced Technology Initiative in Infectious Diseases (2001-2014).
This is a select subset of the publications that have led up to
the development of CANDO (currently going from v2 to v3) including its
key tenets. See also all our
publications related to therapeutic discovery which includes
applications of CANDO as well as a comprehensive list of
more than 140 of our publications.
- Schuler J, Falls Z, Mangione W, Hudson M, Bruggemann L,
Samudrala R. Evaluating performance of drug repurposing
technologies. Drug Discovery Today 2020. invited;
under revision. https://doi.org/10.1101/2020.12.03.410274
- Mangione W, Falls Z, Chopra G, Samudrala R. cando.py:
Open source software for predictive bioanalytics of large scale
drug-protein-disease data. Journal of Chemical Information
and Modeling 60: 4131-4136, 2020.
- Mangione W, Falls Z, Melendy T, Chopra G, Samudrala R.
Shotgun
drug repurposing biotechnology to tackle epidemics and
pandemics. Drug Discovery Today 25: 1126-1128,
2020.
- Fine J, Konc J, Samudrala R, Chopra G. CANDOCK:
Chemical atomic network-based hierarchical flexible docking
algorithm using generalized statistical
potentials. Journal of Chemical Information and
Modeling 60: 1509-1527, 2020.
- Fine J, Lackner R, Samudrala R, Chopra G. Computational
chemoproteomics to understand the role of selected psychoactives in
treating mental health indications. Scientific
Reports 9, 1315, 2019.
- Schuler J, Samudrala R. Fingerprinting
CANDO: Increased accuracy with structure and ligand based shotgun
drug repurposing. ACS Omega 4: 17393-17403, 2019.
- Falls Z, Mangione W, Schuler J, Samudrala R. Exploration
of interaction scoring criteria in the CANDO platform. BMC
Research Notes 12: 318, 2019.
- Mangione W, Samudrala R. Identifying protein features
responsible for improved drug repurposing accuracies using the CANDO
platform: Implications for drug design. Molecules 24: 167,
2019.
- Chopra C, Kaushik S, Elkin PL, Samudrala R. Combating
Ebola with repurposed therapeutics using the CANDO
platform. Molecules 21: 1537, 2016.
- Chopra G, Samudrala R. Exploring polypharmacology in
drug discovery and repurposing using the CANDO
platform. Current Pharmaceutical Design 22: 3109-3123
2016.
- Sethi G, Chopra G, Samudrala R. Multiscale
modelling of relationships between protein classes and drug behavior
across all diseases using the CANDO platform. Mini Reviews
in Medicinal Chemistry 15: 705-717, 2015.
- Minie M, Chopra G, Sethi G, Horst J, White G, Roy A, Hatti K,
Samudrala R. CANDO and
the infinite drug discovery frontier. Drug Discovery
Today 19: 1353-1363, 2014.
- Horst JA, Laurenzi A, Bernard B, Samudrala R. Computational
multitarget drug discovery. Polypharmacology
263-301, 2012.
- Jenwitheesuk E, Horst JA, Rivas K, Van Voorhis WC, Samudrala
R. Novel
paradigms for drug discovery: Computational multitarget
screening. Trends in Pharmacological Sciences 29:
62-71, 2008.
- Jenwitheesuk E, Samudrala R. Identification
of potential multitarget antimalarial drugs. Journal of
the American Medical Association 294: 1490-1491, 2005.
- Jenwitheesuk E, Samudrala R. Improved
prediction of HIV-1 protease-inhibitor binding energies by molecular
dynamics simulations. BMC Structural Biology 3: 2,
2003.
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