Presentation given by Rafael Gozalbes from ProtoQSAR SL in the framework of the Emergence Forum Barcelona Biocat organized the Barcelona Emergence Forum (April 10-11th, 2014, Congress Palace, Montjuïc) supported by the TRANSBIO SUDOE, a translational cooperation project dedicated to innovation in life sciences in South-West Europe. The Barcelona Emergence Forum contributed to bringing together Academics, Companies, Investment Entities, Technology Platforms and Technology Transfer Offices from Spain, France and Portugal to set up collaborative projects on Human Health & Agro-food Innovation. More information at: http://www.b2match.eu/emergenceforum2014

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ProtoQSAR SL
Vivero de Empresas “Creix”
Paseo de la Pechina 15
46008-Valencia, Spain
info@protoqsar.com

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The company
 Creation: September 2012. Two PhD, > 15 years of activity in this field, both at
the academic and industrial sides.
 Objective: application of computational methods to the prediction of physico-
chemical and/or biological properties of chemicals.
 Main work area: "Drug Discovery".
 Other areas: nutraceuticals, pesticides, nanomaterials, REACH legislation.
 Dissemination: > 70 peer reviewed papers in scientific journals, 7 chapters in
specialized books, > 40 presentations in national/international scientific
conferences.

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In silico success in DD
 Norfloxacin, the first fluoroquinolone antibiotic (Koga et al., J. Med. Chem., 1980), QSAR
models developed to predict the best substituents.
 Losartan, the first non-peptide oral angiotensin II antagonist (Duncia et al., J. Med. Chem.,
1990), alignment with a solution structure of angiotensin II.
 5-HT1B/1D agonist Zolmitriptan, indicated for migraine (Glen et al., J. Med. Chem. 1995), based
on a pharmacophore approach & ADME optimised by QSAR.
 Dorzolamide, a carbonic anhydrase inhibitor used to treat glaucoma (Greer et al., J. Med.
Chem., 1994), ab initio calculations suggested the best conformation.
 Antivirals such as Zanamivir (first neuraminidase inhibitor for tratment of influenza) (M.
von Itzstein et al., Nature, 1993) or Amprenavir (HIV-1 protease inhibitor) (Kim et al., J. Amer. Chem.
Soc., 1995).
 Others: H2-receptor antagonist Cimetidine, Probenecid, many of the atypical
antipsychotics, selective COX-2 inhibitors NSAIDs, selective serotonin reuptake
inhibitors (antidepressants), etc.

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Services
 Chemoinformatics:
 Calculating molecular descriptors, filtering chemicals by applying
standardized rules, similarity and/or chemical diversity analysis, etc.
 QSAR/QSPR.
 Molecular modeling:
 Homology modeling.
 Pharmacophore modeling.
 Docking.
 Molecular dynamics.

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Virtual Screening
Target
Docking“De novo” designknown
Pharmacophores
QSAR
CC & HTSunknown
knownunknown
Ligand(s)Available structure:
Virtual screening

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Virtual Screening
Docking
Ligand
Protein
Docking
ProteinLibrary

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Computational chemistry advantages
 Demonstrated good predictivity when selecting/optimizing new chemical
entities.
 Great saving of time, resources and money.
 Easy and rapid applicability of the models to new structures or molecular
collections.
 Limitations on animal testing (3Rs), according to European legislation.

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QSAR/QSPR
 We are particularly featured for our expertise in the development of
mathematical predictive models of structure-activity relationships (QSAR) or
structure-property relationships (QSPR).
 These computational models are of great interest when you have
activity/property data for a number of compounds and the structure of the
therapeutic target is unknown.
 Our company is possibly the only one in Spain providing currently a
specific and specialized service in the QSAR field.

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QSAR
Compounds database
Training set
Validation set
Exp.
activity
Descriptors calculation
ACTIVITY = ∑ ci DESCRIPTORi
n
i = 0
Molecular structures
Structures ID MW Num_carbons Num_heteroatoms Carbon-hetero_ratio Halide_count a_acc diameter a_nH Solubility (mg/L)
O(CC(O)CNC(C)C)c1ccc(NC(=O)CCC)cc1C(=O)CAcebutolol 336,43 18 6 3,00 0 5 15 28 590,50
Clc1cccc(Cl)c1Nc1ccccc1CC(OCC(O)=O)=OAceclofenac 354,18 16 7 2,29 2 3 12 13 223,10
Oc1ccc(NC(=O)C)cc1Acetaminophen 151,16 8 3 2,67 0 2 7 9 0,90
OC(=O)C=Cc1nc(ccc1)/C(=C/CN1CCCC1)/c1ccc(cc1)CAcrivastine 348,44 22 4 5,50 0 4 12 24 57,20
O=C1NC(=Nc2n(cnc12)COCCO)NAcyclovir 225,20 8 8 1,00 0 5 9 11 6,15
O=C1N(N=NN1CC)CCN1CCC(N(C(=O)CC)c2ccccc2)(CC1)COCAlfentanil 416,52 21 9 2,33 0 6 15 32 4008,80
O(CC(O)CNC(C)C)c1ccccc1CC=CAlprenolol 249,35 15 3 5,00 0 3 11 23 420,00
O=C1N(N(C)C(C)=C1N(C)C)c1ccccc1Aminopyrine 231,29 13 4 3,25 0 1 8 17 444,20
S1SCC(NC(=O)C(NC(=O)C(NC(=O)C(NC(=O)C(NC(=O)C(N)C1)Cc1ccc(O)cc1)Cc1ccccc1)CCC(=O)N)CC(=O)N)C(=O)N1CCCC1C(=O)NC(CCCNC(N)=N)C(=O)NCC(=O)NArgipressin 1084,23 46 29 1,59 0 14 26 65 685,30
S1[C@H]2N([C@@H](C(O)=O)C1(C)C)C(=O)[C@H]2NC(=O)Cc1ccccc1Benzylpenicillin 334,39 16 7 2,29 0 4 12 18 66,50
Clc1cc(ccc1)C(=O)C(NC(C)(C)C)CBupropion 239,74 13 3 4,33 1 2 8 18 38,20
S1[C@H]2N(C(C(O)=O)=C(C1)CSc1nn[nH]c1)C(=O)[C@H]2NC(=O)C(N)c1ccc(O)cc1Cefatrizine 462,50 18 13 1,38 0 9 17 18 6,12
s1cc(nc1N)/C(=N/OC)/C(=O)N[C@H]1[C@H]2SCC(CSC3=NC(=O)C(O)=NN3C)=C(N2C1=O)C(O)=OCeftriaxone 554,58 18 18 1,00 0 10 18 18 2315,40
S1[C@H]2N(C(C(O)=O)=C(C1)COC(=O)N)C(=O)[C@H]2NC(=O)C(=N/OC)c1occc1Cefuroxime 424,39 16 13 1,23 0 6 14 16 2232,40
S1[C@H]2N(C(C(O)=O)=C(C1)C)C(=O)[C@H]2NC(=O)[C@H](N)c1ccccc1Cephalexin 347,39 16 8 2,00 0 5 12 17 657,10
Structures ID MW Num_carbons Num_heteroatoms Carbon-hetero_ratio Halide_count a_acc diameter a_nH Solubility (mg/L)
ClC(Cl)C(=O)N[C@@H]([C@H](O)c1ccc([N+](=O)[O-])cc1)COChloramphenicol 323,13 11 9 1,22 2 3 11 12 ???
Clc1cc2N(c3c(Sc2cc1)cccc3)CCCN(C)CChlorpromazine 318,86 17 4 4,25 1 1 9 19 ???
Fc1cc2c(N(C=C(C(O)=O)C2=O)C2CC2)cc1N1CCNCC1Ciprofloxacin 331,34 17 7 2,43 1 4 11 18 ???
Clc1cccc(Cl)c1NC=1NCCN=1Clonidine 230,09 9 5 1,80 2 1 7 9 ???
O=C1CC[C@@]2([C@@H]3[C@H]([C@@H]4CC[C@](O)(C(=O)CO)[C@]4(CC3=O)C)CCC2=C1)CCortisone 360,44 21 5 4,20 0 5 12 28 ???
o1ncc2C[C@@]3([C@@H]4[C@H]([C@@H]5CC[C@@](O)(C#C)[C@]5(CC4)C)CCC3=Cc12)CDanazol 337,46 22 3 7,33 0 2 11 27 ???
N(CCCN1c2c(CCc3c1cccc3)cccc2)CDesipramine 266,38 18 2 9,00 0 1 9 22 ???
Virtual screening

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Available QSAR models
 Physico-chemical parameters: cLogP, PSA, water solubility…
 ADME properties: oral bioavailability, BBB penetration, Caco-2 permeability,
binding to plasma proteins, cytochromes inhibition, distribution volume,
biological half-time…
 Toxicological properties: acute oral toxicity, skin irritation, mutagenicity,
carcinogenicity…
 “Global models”: GPCRs, CNS model.

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Customers & collaborators
Clientes y/o colaboradores.
Mantenemos colaboraciones con grupos de trabajo muy diferentes, tanto por el tipo de
entidad (grupos académicos, entidades privadas sin ánimo de lucro, empresas) como por su
objeto (investigación en química médica y/o combinatoria, en predicción de propiedades
ADME-T, desarrollo de nuevos insecticidas, de nuevos materiales de embalaje, etc.).
Algunos de nuestros clientes son los siguientes:
We collaborate with very different kind of groups, either concerning their entity
(academic groups, private foundations, companies…) or their objective
(medicinal or combinatorial chemistry research, ADME-T prediction, new
insecticides development, new materials for chemical industries, etc.)

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How to work with us
 Application of our QSAR/QSPR models for prediction of properties to
compounds submitted by our customers.
 Hiring of our services in molecular modeling and chemoinformatics (medicinal
chemistry projects, generation of targeted librarires, etc).
 Collaboration in research projects.

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Perspectives
 Functional food: project with a biotech to search for new peptides preventing
metabolic syndrome.
 Agro: collaboration with a private scientific association & INRA to search for red
palm weevil repellents.
 miRNAs: collaboration with a BioDonostia group to analyze the possible role
of miRNAs in multiple sclerosis.
 NanoQSAR: project with a technological institute to develop innovative models
predicting ecotoxicity of nanoparticles.

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Thank you for your
attention!
info@protoqsar.com