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President of the International Academy of Mathematical Chemistry, president of the Italian Chemometric Society and "ad honorem" professor of the University of Azuay Cuenca, Ecuador , he is author of more than publications in international journals and of the books "The Data Analysis Handbook", by I.

Frank and R. Todeschini, ; and "Handbook of Molecular Descriptors", by R.

chapter and author info

Todeschini and V. Consonni, Viviana Consonni received her PhD in chemical sciences from the University of Milano in and is now full researcher of chemometrics and chemoinformatics at the Department of Environmental Sciences of the University of Milano-Bicocca Milano, Italy. She is author of more than 40 publications in peer-reviewed journals and of the book "Handbook of Molecular Descriptors," by R. The enumeration of chemical space. Template-based combinatorial enumeration of virtual compound libraries for lipids.

PDF Burger's Medicinal Chemistry and Drug Discovery 6 Volume Set EBook

J Cheminform. Evaluation of a focused virtual library of heterobifunctional ligands for Clostridium difficile toxins. Org Biomol Chem. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. The Alexandria library, a quantum-chemical database of molecular properties for force field development.

Sci Data. Todeschini R, Consonni V. Handbook of molecular descriptors. Planey SL, Kumar R. Lipophilicity indices for drug development. J Appl Biopharm Pharmacokinet. Sachem: a chemical cartridge for high-performance substructure search. Evans DA. History of the Harvard ChemDraw project. Angew Chem Int Ed. ChemMine tools: an online service for analyzing and clustering small molecules. Nucleic Acids Res. Comparative analysis of chemical similarity methods for modular natural products with a hypothetical structure enumeration algorithm. Getting SMARt in drug discovery: chemoinformatics approaches for mining structure—multiple activity relationships.

RSC Adv. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. The rise of deep learning in drug discovery. Drug Discov Today.

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Hessler G, Baringhaus K. Artificial intelligence in drug design. Data mining and computational modeling of high-throughput screening datasets. Methods in Molecular Biology. Heidelberg: Springer, — Molecular descriptors for structure—activity applications: a hands-on approach. Computational toxicology. An in silico approach towards the prediction of druglikeness properties of inhibitors of plasminogen activator inhibitor1.


Adv Bioinf. Novel natural-product-like caged xanthones with improved druglike properties and in vivo antitumor potency. Bioorg Med Chem Lett. Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling. Proceedings of the National Academy of Sciences. Screening and functional profiling of small-molecule HIV-1 entry and fusion inhibitors.

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Assay Drug Dev Technol. The discovery of novel HDAC3 inhibitors via virtual screening and in vitro bioassay. J Enzyme Inhib Med Chem.

Practical application of ligand efficiency metrics in lead optimisation. Ligand efficiency: a useful metric for lead selection. Fragment-based lead discovery: leads by design. Assessing the lipophilicity of fragments and early hits.

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  • J Comput Aided Mol Des. Leeson PD, Springthorpe B. The influence of drug-like concepts on decision-making in medicinal chemistry. Impact of lipophilic efficiency on compound quality. J Med Chem.

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    Defining desirable central nervous system drug space through the alignment of molecular properties, in vitro ADME, and safety attributes. ACS Chem Neurosci. The influence of lead discovery strategies on the properties of drug candidates. Prioritization of anti-malarial hits from nature: chemo-informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti-malarial drugs. Malar J. Larose DT. Discovering knowledge in data: an introduction to data mining.

    Hoboken, New Jersey. Data mining and clustering in chemical process databases for monitoring and knowledge discovery. J Process Control. In: Bajorath J, Bajorath J, editor s. Chemoinformatics and computational chemical biology. Totowa, New Jersey: Humana Press, In particular, the use of drug utilization data is of utmost importance to calculate reporting rates for drugs with already known association with the event, thus estimating the lowest incidence assuming the under-reporting is limited due to the notoriety of the ADR or at least equally distributed within the database.

    Recently, the publication of PhV analyses through disproportionality measures has been subject to debate and criticism [ 57 ]. Some benefits and strengths of using DMAs are undisputed, since they are quick and inexpensive analyses routinely performed by regulators and researchers for drug safety evaluation [ 16 ]. Apart from the hypothesis-generating purpose of signal detection, other important application of this method are a validation of a pharmacological hypothesis about the mechanism of occurrence of ADRs [ 58 ]; b characterization of the safety profile of drugs [ 59 ].

    Nevertheless, it is important to underline that the identification of potential safety issues does not necessarily imply the need for wider communication to healthcare professional through publication, as it may cause unnecessary alarm without a real safety alert. This would ensure dissemination of high quality studies, which offer innovative methodology or provide novel insight into drug safety.

    The transparency policy recently adopted by the FDA is important to share with pharmacovigilance experts current safety issues requiring close monitoring before publicly disseminating results to consumers. Likewise, disproportionality analyses submitted for publication to relevant journals should address and optimistically try to circumvent bias related to selective reporting, in order to provide meaningful comparison among drugs and allow provisional risk stratification.

    Drug Discovery and Development Process - Clinical Research -

    When planning a pharmacovigilance analysis and discussing its results, there are a number of limitations requiring careful consideration in view of the potential clinical implications. These caveats are related both to data source, namely individual reports with relevant spontaneous reporting system, and the adopted DMA [ 48 , 60 ].

    Underreporting is one the most important limitation on SRS as it prevents to precisely calculate the real incidence of the event in the population [ 61 ]. As a matter of fact, only a small proportion of the ADRs occurring in daily practice is reported. Substantial deficits in data quality and data distortion occur at two levels in SRS databases: at the level of the individual case records i. The FDA, for example, requires the reporting of events that occur in other countries only if they are serious and unlabeled based on the US label.

    Hence a drug for which a serious adverse event has been included in the label earlier will appear to have fewer such events in the FDA AERS database than one for which the event was added later. The geographic distribution of market penetration of a new drug may also influence the apparent safety profile based on the SRS. For example, a drug whose use is predominantly in the U. Apart from the 6-month lag time in data release through the FDA website, the most significant caveats concern the presence of duplicate reports and missing data as well as the lack of standardization in recording drug names of active substances.

    There should also be a commitment to improving the quality of the data, which is ultimately the rate limiting step. A recent study address some challenges and limitations of current pharmacovigilance processes in terms of data completeness and resorted to use the case of flupirtine to exemplify the need for refined ADR reporting [ 62 ]. The pattern of reporting is also widely influenced by external factors, which may affect the reliability of detected signal. Among the most important confounders, the product age i. It is widely accepted that when a drug first receives marketing authorization, there is generally a substantial increase in the spontaneous reporting of ADRs especially during the first two years on the market , which then plateaus and eventually declines.

    This aspect may be related to the increased attention of clinicians towards a novel drug and may intuitively imply that the number of new signals detected reaches a peak over time with a subsequent decline.

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    However, a new therapeutic indication or dose regimen may result in a new reporting pattern, thus one should be aware of the lifecycle status of the drug under investigation as well as significant change in its use. In addition, media attention and publicity resulting from advertising or regulatory actions e. Routine incorporation of time-trend axis should be therefore recommended when planning pharmacovigilance analysis to gain insight into the temporal appearance of the signal, especially when regulatory interventions may have affected the life cycle of drug.

    Even if all the studied drugs are similarly affected by notoriety at a given time, false positive signals could be generated if this notoriety effect on reporting is differentially diluted among prior reports for older drugs compared with more recently marketed drugs.