The use of 2D fingerprint methods to support the assessment of structural similarity in orphan drug legislation

BackgroundIn the European Union, medicines are authorised for some rare disease only if they are judged to be dissimilar to authorised orphan drugs for that disease. This paper describes the use of 2D fingerprints to show the extent of the relationship between computed levels of structural similarity for pairs of molecules and expert judgments of the similarities of those pairs. The resulting relationship can be used to provide input to the assessment of new active compounds for which orphan drug authorisation is being sought.Results143 experts provided judgments of the similarity or dissimilarity of 100 pairs of drug-like molecules from the DrugBank 3.0 database. The similarities of these pairs were also computed using BCI, Daylight, ECFC4, ECFP4, MDL and Unity 2D fingerprints. Logistic regression analyses demonstrated a strong relationship between the human and computed similarity assessments, with the resulting regression models having significant predictive power in experiments using data from submissions of orphan drug medicines to the European Medicines Agency. The BCI fingerprints performed best overall on the DrugBank dataset while the BCI, Daylight, ECFP4 and Unity fingerprints performed comparably on the European Medicines Agency dataset.ConclusionsMeasures of structural similarity based on 2D fingerprints can provide a useful source of information for the assessment of orphan drug status by regulatory authorities.

[1]  Peter Willett,et al.  Combination of Similarity Rankings Using Data Fusion , 2013, J. Chem. Inf. Model..

[2]  Johann Gasteiger,et al.  Structure and reaction based evaluation of synthetic accessibility , 2007, J. Comput. Aided Mol. Des..

[3]  Jürgen Bajorath,et al.  Similarity searching , 2011 .

[4]  The Committee for Orphan Medicinal Products and the Eu Secretariat European regulation on orphan medicinal products: 10 years of experience and future perspectives , 2011, Nature Reviews Drug Discovery.

[5]  E. Tambuyzer,et al.  Rare diseases, orphan drugs and their regulation: questions and misconceptions , 2010, Nature Reviews Drug Discovery.

[6]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[7]  Menfo A Imoisili,et al.  The routes to orphan drug designation--our recent experience at the FDA. , 2012, Drug discovery today.

[8]  J. Bajorath,et al.  State-of-the-art in ligand-based virtual screening. , 2011, Drug discovery today.

[9]  Paola Gramatica,et al.  Principles of QSAR models validation: internal and external , 2007 .

[10]  Pascal Bonnet,et al.  Is chemical synthetic accessibility computationally predictable for drug and lead-like molecules? A comparative assessment between medicinal and computational chemists. , 2012, European journal of medicinal chemistry.

[11]  David Ellis,et al.  On the Creation of Hypertext Links in Full-Text Documents: Measurement of Inter-Linker Consistency , 1994, J. Documentation.

[12]  Peter Willett,et al.  Similarity methods in chemoinformatics , 2009, Annu. Rev. Inf. Sci. Technol..

[13]  Paolo Massimo Buscema,et al.  Similarity Coefficients for Binary Chemoinformatics Data: Overview and Extended Comparison Using Simulated and Real Data Sets , 2012, J. Chem. Inf. Model..

[14]  Jérôme Hert,et al.  Turbo similarity searching: Effect of fingerprint and dataset on virtual‐screening performance , 2009, Stat. Anal. Data Min..

[15]  Mirja Iivonen,et al.  Consistency in the Selection of Search Concepts and Search Terms , 1995, Information Processing & Management.

[16]  A. Tropsha,et al.  Beware of q 2 , 2002 .

[17]  George W. Adamson,et al.  A Comparison of the Performance of Some Similarity and Dissimilarity Measures in the Automatic Classification of Chemical Structures , 1975, J. Chem. Inf. Comput. Sci..

[18]  N. Obuchowski,et al.  Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures , 2010, Epidemiology.

[19]  Joel Lexchin,et al.  The cost of drug development: a systematic review. , 2011, Health policy.

[20]  David Rogers,et al.  Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..

[21]  Tudor I. Oprea,et al.  A crowdsourcing evaluation of the NIH chemical probes. , 2009, Nature chemical biology.

[22]  Michael S Lajiness,et al.  Assessment of the consistency of medicinal chemists in reviewing sets of compounds. , 2004, Journal of medicinal chemistry.

[23]  David S. Wishart,et al.  DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..

[24]  Robert J. W. Tijssen,et al.  A scientometric cognitive study of neural network research: Expert mental maps versus bibliometric maps , 1993, Scientometrics.

[25]  Irena Melnikova,et al.  Rare diseases and orphan drugs , 2012, Nature Reviews Drug Discovery.

[26]  Zachary Estes,et al.  Individual differences in the perception of similarity and difference , 2008, Cognition.

[27]  K. Markey Interindexer consistency tests: a literature review and report of a test of consistency in indexing visual materials , 1984 .

[28]  Nikolaus Stiefl,et al.  Structural resemblances and comparisons of the relative pharmacological properties of imatinib and nilotinib. , 2010, Bioorganic & medicinal chemistry.

[29]  Dimitris K. Agrafiotis,et al.  Library Enhancement through the Wisdom of Crowds , 2011, J. Chem. Inf. Model..

[30]  J. Arrowsmith,et al.  Orphan drug development: an economically viable strategy for biopharma R&D. , 2012, Drug discovery today.

[31]  Meir Glick,et al.  Inside the Mind of a Medicinal Chemist: The Role of Human Bias in Compound Prioritization during Drug Discovery , 2012, PloS one.

[32]  Pedro Franco,et al.  Orphan drugs: the regulatory environment. , 2013, Drug discovery today.