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Differential gene expression analysis

Differential expression analysis consists in taking the normalised read count data from RNA-Seq, microArrays or TempO-seq data to performing statistical analysis to discover quantitative changes in expression levels between experimental groups.
For example, we use statistical testing to decide whether, for a given gene, an observed difference in read counts is significant, that is, whether it is greater than what would be expected just due to natural random variation.
We also can apply machine learning models to the gene expression profiles in order to build predictive models for a specific disease or treatment. Input data can be from human samples, or from in vivo and in vitro models. Our expertise also includes the performance of suitable statistical tests for biomarker confirmation, the identification of pathways that are related with the detected molecular changes and an optimal visualization of the results.

Services offered:

  • Scientific contributions

  • Besalú E, De Julián-Ortiz JV. Ranking Series of Cancer-Related Gene Expression Data by Means of the Superposing Significant Interaction Rules Method. Biomolecules. 2020 Sep 8;10(9):1293. doi: 10.3390/biom10091293. PMID: 32911598; PMCID: PMC7564041.
  • Proaño-Pérez E, Serrano-Candelas E, Mancia C, Navinés-Ferrer A, Guerrero M, Martin M. SH3BP2 Silencing Increases miRNAs Targeting ETV1 and Microphthalmia-Associated Transcription Factor, Decreasing the Proliferation of Gastrointestinal Stromal Tumors. Cancers (Basel). 2022 Dec 15;14(24):6198. doi: 10.3390/cancers14246198. PMID: 36551682; PMCID: PMC9777313.

Homology modelling

Homology modelling is a computational method to predict the 3D structure of proteins through the sequence alignment of template proteins with known structure. Generally, the process consists in four steps: target identification, sequence alignment, model building and model refinement.
Homology modeling can guide mutagenesis experiments or hypotheses about structure-function relationships. In drug discovery and development, homology modelling is useful for the identification of new targets and can be applied for the realization of virtual screening for hit/lead generation if the sequence identity with the template is >30%.

Services offered:

  • Study and selection of template proteins
  • Sequence alignment and model building
  • Model refinement by molecular dynamics
  • Phylogenetic analysis
  • Scientific contributions

  • Serrano-Candelas E, Farré D, Aranguren-Ibáñez Á, Martínez-Høyer S, Pérez-Riba M. The vertebrate RCAN gene family: novel insights into evolution, structure and regulation. PLoS One. 2014 Jan 20;9(1):e85539. doi: 10.1371/journal.pone.0085539. PMID: 24465593; PMCID: PMC3896409.

Macromolecule interaction network

Protein-protein interaction (PPI) networks are mathematical representations of the physical contacts between the proteins in a cell, called the protein ineractome. The regulation of protein function through protein-protein interactions is the basis of most biological activity in living cells, and more than 650000 types of specific protein-protein interactions are estimated to take place in each human cell. In disease, these biological functions may be altered.
Also protein-RNA/DNA interaction networks play a central role in many fundamental cellular processes, such as transcription, recombination and replication. For instance, in gene regulation, physical interactions and reactions among the molecular components together with the physical properties of DNA control how genes are turned on and off.
Thus, identifying and understanding protein-protein and protein-DNA interactions can help to understand the mechanisms that trigger the beginning and progressing of a disease, and identify potential targets for modifying biological functions by the use of drugs.

Services offered:

  • PPI interactions
  • miRNA-target interaction
  • Gene-gene interaction
  • Identification of Transcription Factor Binding Sites (TFBS)
  • Scientific contributions

  • Serrano-Candelas E, Farré D, Aranguren-Ibáñez Á, Martínez-Høyer S, Pérez-Riba M. The vertebrate RCAN gene family: novel insights into evolution, structure and regulation. PLoS One. 2014 Jan 20;9(1):e85539. doi: 10.1371/journal.pone.0085539. PMID: 24465593; PMCID: PMC3896409.
  • Proaño-Pérez E, Serrano-Candelas E, Mancia C, Navinés-Ferrer A, Guerrero M, Martin M. SH3BP2 Silencing Increases miRNAs Targeting ETV1 and Microphthalmia-Associated Transcription Factor, Decreasing the Proliferation of Gastrointestinal Stromal Tumors. Cancers (Basel). 2022 Dec 15;14(24):6198. doi: 10.3390/cancers14246198. PMID: 36551682; PMCID: PMC9777313.

Metabolomics profiling

Metabolomics profiling is a powerful tool to perform a comprehensive quantitative analysis of the end products of the metabolic pathways in biological systems, providing valuable information on the biochemical processes occurring in the body. In disease, metabolic alterations can reflect changes occurring at the origin of the disorder and provide relevant information regarding condition-associated mechanisms and potential drug targets.
Metabolomics analysis can be performed on very different kinds of samples from humans, animal or cellular models, including biofluids, cell and tissue samples. As a results of these kind of studies, large data tables are generated, that have to be analyzed in correlation with different study factors, such as the presence of disease, mutations, treatments, ambient conditions, etc. to extract relevant features, that can later be confirmed as biomarkers for a specific condition.
We have a large experience in the curation and filtering of large data sets, and in the application of machine learning models to filter variables that are significant for the prediction of a specific parameter. Our expertise also includes the performance of suitable statistical tests for biomarker confirmation, the identification of pathways that are related with the detected molecular changes and an optimal visualization of the results.

Services offered:

  • Scientific contributions

  • Biagetti B, Herance JR, Ferrer R, Aulinas A, Palomino-Schätzlein M, Mesa J, Castaño JP, Luque RM, Simó R. Metabolic Fingerprint of Acromegaly and its Potential Usefulness in Clinical Practice. J Clin Med. 2019 Sep 26;8(10):1549. doi: 10.3390/jcm8101549. PMID: 31561638; PMCID: PMC6832216.
  • Tapia A, Giachello CN, Palomino-Schätzlein M, Baines RA, Galindo MI. Generation and Characterization of the Drosophila melanogaster paralytic Gene Knock-Out as a Model for Dravet Syndrome. Life (Basel). 2021 Nov 18;11(11):1261. doi: 10.3390/life11111261. PMID: 34833136; PMCID: PMC8619338.
  • Palomino-Schätzlein M, Pineda-Lucena A. Metabolomic Applications to the Characterization of the Mode-of-Action of CDK Inhibitors. Methods Mol Biol. 2016;1336:211-23. doi: 10.1007/978-1-4939-2926-9_16. PMID: 26231718.

Pathway analysis

Pathway analysis can identify affected metabolic routes with the help of high troughput biological information of different phenotypes. For instance, with data from a differential gene expression analysis or metabolomics profiling, a specific list of proteins or metabolites can be identified, that is correlated with the genes through metabolic pathways. Different statistical criteria are employed to confirm the routes that are significantly altered.
This kind of analysis can be a suitable tool to identify new targets for drug discovery, when we identify the pathways that are altered by a specific disease, and the enzymes that are involved in it. In clinical and preclinical studies, a pathway analysis after a specific treatment, can help us to identify the mechanism of actions of a specific drug, which can be related with its efficiency or possible side effects.

Services offered:

  • Gene pathway analysis
  • Proteomic pathway analysis
  • Metabolomics pathway analysis
  • Integrated pathway analysis
  • Pathway mapping visualitzation
  • Scientific contributions

  • Palomino-Schätzlein M, Lamas-Domingo R, Ciudin A, Gutiérrez-Carcedo P, Marés R, Aparicio-Gómez C, Hernández C, Simó R, Herance JR. A Translational In Vivo and In Vitro Metabolomic Study Reveals Altered Metabolic Pathwaysin Red Blood Cells of Type 2 Diabetes. J Clin Med. 2020 May 27;9(6):1619. doi: 10.3390/jcm9061619. PMID: 32471219; PMCID: PMC7355709.

Virtual screening

Virtual screening is an optimal in silico approaches for modeling molecular interactions for in target and lead discovery processes. These kind of computational techniques allow to obtain valuable data about the binding of two structures (ligand and target) and provide relevant information about the possible mechanism of action of biomolecules related to different diseases.
Docking can be performed on very different kinds of biomolecules from different species, providing information about different types of interactions as small molecules-protein, protein-protein, small molecules-DNA/RNA or protein DNA/RNA.

Services offered:

  • Docking
  • Ligand-target binding rationalization
  • Docking-based virtual screening
  • Interaction’s analysis
  • Drug repositioning
  • Target fishing
  • Scientific contributions

  • Gozalbes R, Simon L, Froloff N, Sartori E, Monteils C, Baudelle R. Development and experimental validation of a docking strategy for the generation of kinase-targeted libraries. J Med Chem. 2008 Jun 12;51(11):3124-32. doi: 10.1021/jm701367r. Epub 2008 May 15. PMID: 18479119.
  • Goya-Jorge E, Abdmouleh F, Carpio LE, Giner RM, Sylla-Iyarreta Veitía M. Discovery of 2-aryl and 2-pyridinylbenzothiazoles endowed with antimicrobial and aryl hydrocarbon receptor agonistic activities. Eur J Pharm Sci. 2020 Aug 1;151:105386. doi: 10.1016/j.ejps.2020.105386. Epub 2020 May 27. PMID: 32470576; PMCID: PMC7251408.
  • Goya-Jorge E, Rampal C, Loones N, Barigye SJ, Carpio LE, Gozalbes R, Ferroud C, Sylla-Iyarreta Veitía M, Giner RM. Targeting the aryl hydrocarbon receptor with a novel set of triarylmethanes. Eur J Med Chem. 2020 Dec 1;207:112777. doi: 10.1016/j.ejmech.2020.112777. Epub 2020 Sep 2. PMID: 32971427.
  • Moasses Ghafary S, Soriano-Teruel PM, Lotfollahzadeh S, Sancho M, Serrano-Candelas E, Karami F, Barigye SJ, Fernández-Pérez I, Gozalbes R, Nikkhah M, Orzáez M, Hosseinkhani S. Identification of NLRP3PYD Homo-Oligomerization Inhibitors with Anti-Inflammatory Activity. Int J Mol Sci. 2022 Jan 31;23(3):1651. doi: 10.3390/ijms23031651. PMID: 35163573; PMCID: PMC8835912.

Pharmacophore modeling

A pharmacophore is defined as the ensemble of steric and electronic features of a ligand that is necessary to ensure the optimal supramolecular interactions with a specific biological target. The most commonly used features for describing pharmacophore maps are defined are hydrogen bond acceptors and donors, acidic and basic groups, aliphatic hydrophobic moieties, and aromatic hydrophobic moieties. 3D-pharmacophores can then be used to perform a screening of available databases with docking experiments, a fast method that can extract diverse leads in term of structure. The database can be initially screened for drug-like molecules by applying different rational filters such as the Lipinski’s Rule or drug-like ADME properties.

Services offered:

  • Scientific contributions

  • Gozalbes R, Barbosa F, Nicolaï E, Horvath D, Froloff N. Development and validation of a pharmacophore-based QSAR model for the prediction of CNS activity. ChemMedChem. 2009 Feb;4(2):204-9. doi: 10.1002/cmdc.200800282. PMID: 19097128.
  • Ambure P, Kar S, Roy K. Pharmacophore mapping-based virtual screening followed by molecular docking studies in search of potential acetylcholinesterase inhibitors as anti-Alzheimer's agents. Biosystems. 2014 Feb;116:10-20. doi: 10.1016/j.biosystems.2013.12.002. Epub 2013 Dec 8. PMID: 24325852.
  • Horvath, Dragos & Mao, Boryeu & Rafael, Gozalbes & Barbosa, Frédérique & Rogalski, Sherry. (2005). Strengths and Limitations of Pharmacophore‐Based Virtual Screening. doi: 10.1002/3527603743.ch5.

Fragment based screening

Fragment-based drug discovery is a smart method to find lead compounds, starting from fragments binding weakly to targets and then growing them or combining them to produce a lead with a higher affinity. Fragments are small organic molecules with a low molecular weight. Initially, libraries with a few thousand compounds with molecular weights around 200 Da can be virtually screened, detecting millimolar affinities. Affinities can be experimentally confirmed by NMR based interaction and screening studies. Then, fragments are grown from a bound core by different in silico tools. .

Services offered:

  • In silico fragment library screening
  • NMR-based binding screening
  • Fragment growing in silico assays
  • Scientific contributions

  • Mahmoudi N, de Julián-Ortiz JV, Ciceron L, Gálvez J, Mazier D, Danis M, Derouin F, García-Domenech R. Identification of new antimalarial drugs by linear discriminant analysis and topological virtual screening. J Antimicrob Chemother. 2006 Mar;57(3):489-97. doi: 10.1093/jac/dki470. Epub 2006 Jan 13. PMID: 16415127.
  • Gozalbes R, Carbajo RJ, Pineda-Lucena A. Contributions of computational chemistry and biophysical techniques to fragment-based drug discovery. Curr Med Chem. 2010;17(17):1769-94. doi: 10.2174/092986710791111224. PMID: 20345344.
  • Gozalbes R, Pineda-Lucena A. Small molecule databases and chemical descriptors useful in chemoinformatics: an overview. Comb Chem High Throughput Screen. 2011 Jul;14(6):548-458. doi: 10.2174/138620711795767857. PMID: 21521149.

NMR based interaction and screening

Nuclear Magnetic Resonance (NMR) is a powerful tool to proof the interaction of a selected ligand with a specific target.
The analysis can be performed from the point of view of the target, in general a protein, by analyzing the induced changes in a previously assigned 2D HSQC experiment. In general, this experiment can give as also a clue about the sites of the target that are involved in the interaction. On the other hand, ligand based analysis, such as the STD or the WaterLogsy experiment, require lower sample concentrations, and can give information about the parts of the ligand that interact most. Both experiment types can provide an approximate value of the binding constant, and can be performed as screening experiments with sample batches.

Services offered:

  • 2D 15HSQC protein-ligand experiment
  • STD experiment
  • WaterLogsy experiment
  • Scientific contributions

  • Gozalbes R, Mosulén S, Carbajo RJ, Pineda-Lucena A. Development and NMR validation of minimal pharmacophore hypotheses for the generation of fragment libraries enriched in heparanase inhibitors. J Comput Aided Mol Des. 2009 Aug;23(8):555-69. doi: 10.1007/s10822-009-9269-0. Epub 2009 May 7. PMID: 19421720.
  • Rombouts FJR, Alexander R, Cleiren E, De Groot A, Carpentier M, Dijkmans J, Fierens K, Masure S, Moechars D, Palomino-Schätzlein M, Pineda-Lucena A, Trabanco AA, Van Glabbeek D, Vos A, Tresadern G. Fragment Binding to β-Secretase 1 without Catalytic Aspartate Interactions Identified via Orthogonal Screening Approaches. ACS Omega. 2017 Feb 28;2(2):685-697. doi: 10.1021/acsomega.6b00482. Epub 2017 Feb 24. PMID: 28626832; PMCID: PMC5472370.

(Q)SAR predictions

(Q)SAR modelling has proven to be a fast, economic and versatile option to predict a wide range of properties of chemical compounds. The application of (Q)SAR models in the drug discovery and development process can avoid to perform unnecessary experiments and save time and money. (Q)SAR can contribute to the identification of potential ligands by bioactivity prediction, and help in the lead optimization process by ADME (Absorption, Distribution, Metabolism and Excretion prediction) and toxicity prediction. Furthermore, (Q)SAR predictions can play an important role in the approval of a chemical compound by providing data about is effect on human health and environment, and is one of the recommended tools by the European authorities.
We offer you predictions with our already established models, or to build personalized models for you that are optimized for your compounds and endpoints. Our models strictly follow the OECD principles for regulatory requirements and provide QMRF and QPRF documents

Services offered:

  • Virtual screening
  • Bioactivity prediction
  • Absorption, Distribution, Metabolism and Excretion properties prediction (ADME)
  • Toxicity prediction (human and environmental)
  • Physicochemical properties prediction
  • Scientific contributions

  • Besalú E, Ponec R, de Julián-Ortiz JV. Virtual generation of agents against Mycobacterium tuberculosis. A QSAR study. Mol Divers. 2003;6(2):107-20. doi: 10.1023/b:modi.0000006839.52374.d7. PMID: 14761161.
  • Ambure P, Roy K. Exploring structural requirements of imaging agents against Aβ plaques in Alzheimer's disease: a QSAR approach. Comb Chem High Throughput Screen. 2015;18(4):411-9. doi: 10.2174/1386207318666150305124225. PMID: 25747447.
  • Gozalbes R, Jacewicz M, Annand R, Tsaioun K, Pineda-Lucena A. QSAR-based permeability model for drug-like compounds. Bioorg Med Chem. 2011 Apr 15;19(8):2615-24. doi: 10.1016/j.bmc.2011.03.011. Epub 2011 Mar 12. PMID: 21458999.
  • Rolland C, Gozalbes R, Nicolaï E, Paugam MF, Coussy L, Barbosa F, Horvath D, Revah F. G-protein-coupled receptor affinity prediction based on the use of a profiling dataset: QSAR design, synthesis, and experimental validation. J Med Chem. 2005 Oct 20;48(21):6563-74. doi: 10.1021/jm0500673. PMID: 16220973.

Molecular dynamics

Molecular dynamics (MD) simulations of macromolecular recpetors and their associated small molecule ligands are valuable in silico approaches for modeling molecular interactions and lead discovery processes. Their main advantage is their explicit treatment of macromolecules as dynamic entities in which the internal motions and resulting conformational changes play an essential role in their function. This allows a more accurate estimate of the thermodynamics and kinetics associated with drug–target recognition and binding.
Docking and MD can be performed on very different kinds of biomolecules from different species, providing information about different types of interactions as small molecules-protein, protein-protein, small molecules-DNA/RNA or protein-DNA/RNA.

Services offered:

  • Molecular dynamics
  • Simulation of biomolecules
  • Structure refinement
  • Binding stability studies
  • Scientific contributions

  • Rolland C, Gozalbes R, Nicolaï E, Paugam MF, Coussy L, Barbosa F, Horvath D, Revah F. G-protein-coupled receptor affinity prediction based on the use of a profiling dataset: QSAR design, synthesis, and experimental validation. J Med Chem. 2005 Oct 20;48(21):6563-74. doi: 10.1021/jm0500673. PMID: 16220973.
  • Goya-Jorge E, Abdmouleh F, Carpio LE, Giner RM, Sylla-Iyarreta Veitía M. Discovery of 2-aryl and 2-pyridinylbenzothiazoles endowed with antimicrobial and aryl hydrocarbon receptor agonistic activities. Eur J Pharm Sci. 2020 Aug 1;151:105386. doi: 10.1016/j.ejps.2020.105386. Epub 2020 May 27. PMID: 32470576; PMCID: PMC7251408.
  • Goya-Jorge E, Rampal C, Loones N, Barigye SJ, Carpio LE, Gozalbes R, Ferroud C, Sylla-Iyarreta Veitía M, Giner RM. Targeting the aryl hydrocarbon receptor with a novel set of triarylmethanes. Eur J Med Chem. 2020 Dec 1;207:112777. doi: 10.1016/j.ejmech.2020.112777. Epub 2020 Sep 2. PMID: 32971427.

Structural characterization

Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for the characterization and quantitative analysis of small organic molecules. For structure elucidation, structural connectivities are determined from two-dimensional through-bond correlation experiments. The relative stereochemistry can be deduced from NOE correlations and coupling constants. The final chemical structure can be confirmed by a molecular weight and/or composition analysis. Once the chemical structure is confirmed, the amount of compound in a pure material or a mixture, can be quantified in a simple and fast way. This procedure can also be applied to the identification of impurities and degradation products in bulk drugs and their pharmaceutical formulations. Furthermore, QSAR modelling can be applied to determine the toxicity of the identified impurities.

Services offered:

  • Structure elucidation
  • Quantitative analysis
  • Identification of impurities
  • Authenticity screening
  • QSAR toxicity prediction of impurities
  • Scientific contributions

  • Gómez C, Olano C, Palomino-Schätzlein M, Pineda-Lucena A, Carbajo RJ, Braña AF, Méndez C, Salas JA. Novel compounds produced by Streptomyces lydicus NRRL 2433 engineered mutants altered in the biosynthesis of streptolydigin. J Antibiot (Tokyo). 2012 Jul;65(7):341-8. doi: 10.1038/ja.2012.37. Epub 2012 May 9. PMID: 22569159.
  • Horna DH, Gómez C, Olano C, Palomino-Schätzlein M, Pineda-Lucena A, Carbajo RJ, Braña AF, Méndez C, Salas JA. Biosynthesis of the RNA polymerase inhibitor streptolydigin in Streptomyces lydicus: tailoring modification of 3-methyl-aspartate. J Bacteriol. 2011 May;193(10):2647-51. doi: 10.1128/JB.00108-11. Epub 2011 Mar 11. PMID: 21398531; PMCID: PMC3133142.

In vivo and in vitro experiments

Moldrug works in close cooperation with different companies, universities and research institutions that have an extensive experience in the performance of preclinical and toxicological studies. These studies include in vitro studies with cell or tissue models, protein expression and purification, in vivo assays with animal models or ecotoxicologial assays, among others. Thus, we can complement our computational studies with experimental analyses for toxicity, interaction confirmation, transcriptomics or genomic analysis, cellular screening, etc

Research laboratories that collaborate with Moldrug

  • Scientific contributions

  • Arroyo-Crespo JJ, Armiñán A, Charbonnier D, Deladriere C, Palomino-Schätzlein M, Lamas-Domingo R, Forteza J, Pineda-Lucena A, Vicent MJ. Characterization of triple-negative breast cancer preclinical models provides functional evidence of metastatic progression. Int J Cancer. 2019 Oct 15;145(8):2267-2281. doi: 10.1002/ijc.32270. Epub 2019 Apr 2. PMID: 30860605; PMCID: PMC6767480.
  • Palomino-Schätzlein M, Carranza-Valencia J, Guirado J, Juarez-Carreño S, Morante J. A toolbox to study metabolic status of Drosophila melanogaster larvae. STAR Protoc. 2022 Feb 24;3(1):101195. doi: 10.1016/j.xpro.2022.101195. PMID: 35252884; PMCID: PMC8888985.
  • Palomino-Schätzlein M, Lamas-Domingo R, Ciudin A, Gutiérrez-Carcedo P, Marés R, Aparicio-Gómez C, Hernández C, Simó R, Herance JR. A Translational In Vivo and In Vitro Metabolomic Study Reveals Altered Metabolic Pathwaysin Red Blood Cells of Type 2 Diabetes. J Clin Med. 2020 May 27;9(6):1619. doi: 10.3390/jcm9061619. PMID: 32471219; PMCID: PMC7355709.
  • Serrano-Candelas E, Martínez-Aranguren R, Vega O, Gastaminza G, Bartra J, Audicana MT, Núñez-Córdoba JM, Algorta J, Valero A, Martin M, Ferrer M. Omalizumab efficacy in cases of chronic spontaneous urticaria is not explained by the inhibition of sera activity in effector cells. Sci Rep. 2017 Aug 21;7(1):8985. doi: 10.1038/s41598-017-09361-4. PMID: 28827590; PMCID: PMC5566209.
  • Serrano-Candelas E, Martínez-Aranguren R, Vega O, Gastaminza G, Bartra J, Audicana MT, Núñez-Córdoba JM, Algorta J, Valero A, Martin M, Ferrer M. Omalizumab efficacy in cases of chronic spontaneous urticaria is not explained by the inhibition of sera activity in effector cells. Sci Rep. 2017 Aug 21;7(1):8985. doi: 10.1038/s41598-017-09361-4. PMID: 28827590; PMCID: PMC5566209.

Regulatory requirements and dossier preparation guidance

MolDrug can help you to meet the requirements of different national and international laws, such as EMA regulation, BPR, CLP or ICH. We can assess you with the different steps that has to be conducted, the deadlines, and the parameters that has to be documented for each compound, depending on its chemical nature, its application and the quantity that will be produced.
Our QSAR prediction tools have been specifically designed for their application at regulatory level: They comply strictly with the rules established by the Organization for Economic Co-operation and Development (OECD) for their scientific validation and acceptance for regulatory purposes. Furthermore, all our models are subjected to a double validation, internal and external, and when possible we additionally perform an experimental validation with independent structures belonging to the same chemical space. Our results are presented in adequate standardized formats to facilitate the administrative procedures in the process of regulation/authorization, such as QMRF and QPRF (required in the IUCLID registry tool).
Furthermore our team of experts and external collaborators in chemistry, toxicity and regulatory affairs can provide you with technical help for the preparation of a dossier for different regulations, such as ICH, EMA or CLP.

Services offered:

  • Regulatory requirements for EMA, EFSA, FDA, etc.
  • List of required endpoints
  • Generation and interpretation of QMRF and QPRF files
  • IUCLID tool
  • Preparation of dossiers for ICH-M7, cosmetics and EMA.
  • CLP preparation.

Experimental Design

An appropriate design of the studies is a key element to obtain the desired results. Therefore, we not only assess you with your analysis, but also with the planification of the initialstudy design.
This design will depend on the particular research question and the availability of resources. In the first place, the type (descriptive, observational, experimental), the design (case control, cohort, meta-analysis, etc) and the temporality  (prospective, retrospective, longitudinal) has to be defined. Another important point is the identification of the variables that will be detected, and the number of observations that are required. Furthermore, we have to establish specific protocols to perform our analysis in a reproducible and coherent way, taking into account previous studies, established procedures, etc. Finally, it is important to define specific metrics for the evaluation of the study results, such as sensitivity, specificity, AUC, etc.

Services offered:

  • Study preparation and design
  • Parameters and variable
  • Optimal methodology
  • Result evaluation

Data analysis and visualization

Data preprocessing is an important and time-consuming step when dealing with large data tables from screening, omics or prediction studies. Duplicate, incoherent, inhomogeneous, bad formatting or contradictory data are some of the main problems that should be addressed. Incoming data should be always cleaned and formatted in the best possible way to facilitate the subsequent analysis and maximize the extraction of relevant information.
Another critical step is an optimal presentation and communication of our results. The application of suitable statistical methods and attractive visualization tools could play a key role to extract interesting conclusions from our results. In MoldDrug we are experts in these procedures, and can help you to take the maximum value out of your data.

Services offered:

  • Data cleaning
  • Data processing
  • Data transformation
  • Statistical reports
  • Data visualization
  • Dashboard preparation

Article and project writing

To convert interesting research ideas into a coherent, clear and well written document that fulfills the requirement of the different financing institutions is a difficult task that needs important amount of experience. And once that we have our project results, we want to transfer them to the scientific community in the form of articles or other scientific communications, that have to be reviewed by scientific experts. Therefore, can assist you with the design and writing of your projects, and to convert your results into high quality manuscripts with an optimal structure, didactical illustration and a suitable wording.

Services offered:

  • Project design assistance
  • Project writing and revision
  • Article structure, writing and revision

Target Discovery

Target discovery is the first step of the drug discovery and development scheme, and consists in the identification and early validation of disease-modifying targets, such as proteins or RNA molecules. Once these targets are identified, drug developers can design and test compounds that interact with the targets in a way that modifies the disease process and provide therapeutic benefit.
MolDrug offers a series of services including differential gene expression analysis, homology modelling, protein-protein interaction network analysis, metabolomic profiling and pathway analysis, that are interesting methodologies for the identification of disease-relevant proteins.

Hit to lead

The hit to lead step of the drug discovery process is a crucial phase in which promising compounds are selected as starting points for further development. This step follows the initial screening of large libraries of compounds to identify those with biological activity against a target of interest. Once a hit has been identified, it undergoes a series of tests to assess its potential as a lead compound for drug development.

MolDrug offers a series of services including virtual screening, pharmacophore modelling, fragment based screening, NMR based interaction and screening and QSAR bioactivity, that are can contribute to this step.

Lead optimization

The lead optimization is a critical phase where the most promising compounds are further refined and optimized to improve their efficacy, selectivity, pharmacokinetics, and safety profiles. During lead optimization, medicinal chemists modify the chemical structure of the lead compounds through a series of iterative design and synthesis cycles. The aim is to achieve a balance between the desired pharmacological activity and the potential for unwanted side effects, as well as to optimize the drug’s absorption, distribution, metabolism, and excretion (ADME) properties.

MolDrug offers a series of services including refined docking, fragment based screening, molecular dynamics, structural characterization and QSAR ADMA and toxicity prediction, to perform this step.

(Pre)clinical studies

The (pre)clinical studies step of the drug discovery process involves the rigorous testing of potential drug candidates in laboratory experiments, animal models, and ultimately human clinical trials. These studies aim to assess the safety, efficacy, pharmacokinetics, and toxicology of the drug candidate. In preclinical studies, the candidate compound is evaluated in vitro and in vivo to determine its pharmacological activity, ADME properties, and potential side effects. Animal studies are then conducted to assess the safety and efficacy of the drug candidate in living organisms. Finally, the candidate drug is tested in human clinical trials, starting with small Phase I studies to evaluate safety and tolerability, followed by larger Phase II and III studies to assess efficacy, safety, and optimal dosing.

MolDrug works in collaboration with different laboratories and hospital, were in vitro, in vivo and clinical trials can be carried to provide the necessary data to support regulatory approval and eventual commercialization of the drug.

Regulatory approval

The regulatory approval is the final stage before a drug can be made available to patients. In most countries, regulatory agencies such as the US Food and Drug Administration (FDA) or the European Medicines Agency (EMA) are responsible for evaluating the safety, efficacy, and quality of drugs before they can be marketed. To obtain regulatory approval, drug companies must submit a comprehensive dossier containing preclinical and clinical data, manufacturing information, and labeling information.

MolDrug offers prediction of toxicity and ADMA data that can provide valuable information to evaluate the commerciality of chemical compounds. Moldrug QSAR models follow the 5 principles of the OECD that in silico models have to fulfill to be applicated in a regulatory context.