M. N. D. S. Cordeiro

3D-MEDNEs: An Alternative "in Silico" Technique for Chemical Research in Toxicology. 2. Quantitative Proteome-Toxicity Relationships (QPTR) based on Mass Spectrum Spiral Entropy

Low range mass spectra (MS) characterization of serum proteome offers the best chance of discovering proteome-(early drug-induced cardiac toxicity) relationships, called here Pro-EDICToRs. However, due to the thousands of proteins involved, finding the single disease-related protein could be a hard task. The search for a model based on general MS patterns becomes a more realistic choice. In our previous work ( González-Díaz, H. , et al. Chem. Res. Toxicol. 2003, 16, 1318- 1327 ), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs). In this previous work, quantitative structure-toxicity relationship (QSTR) techniques allowed us to link 3D-MEDNEs with blood toxicological properties of drugs. In this second part, we extend 3D-MEDNEs to numerically encode biologically relevant information present in MS of the serum proteome for the first time. Using the same idea behind QSTR techniques, we can seek now by analogy a quantitative proteome-toxicity relationship (QPTR). The new QPTR models link MS 3D-MEDNEs with drug-induced toxicological properties from blood proteome information. We first generalized Randics spiral graph and lattice networks of protein sequences to represent the MS of 62 serum proteome samples with more than 370 100 intensity ( I i ) signals with m/ z bandwidth above 700-12000 each. Next, we calculated the 3D-MEDNEs for each MS using the software MARCH-INSIDE. After that, we developed several QPTR models using different machine learning and MS representation algorithms to classify samples as control or positive Pro-EDICToRs samples. The best QPTR proposed showed accuracy values ranging from 83.8% to 87.1% and leave-one-out (LOO) predictive ability of 77.4-85.5%. This work demonstrated that the idea behind classic drug QSTR models may be extended to construct QPTRs with proteome MS data.

Chemoinformatics for rational discovery of safe antibacterial drugs: simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals.

Streptococci are a group of Gram-positive bacteria which are responsible for causing many diverse diseases in humans and other animals worldwide. The high prevalence of resistance of these bacteria to current antibacterial drugs is an alarming problem for the scientific community. The battle against streptococci by using antimicrobial chemotherapies will depend on the design of new chemicals with high inhibitory activity, having also as low toxicity as possible. Multi-target approaches based on quantitative-structure activity relationships (mt-QSAR) have played a very important role, providing a better knowledge about the molecular patterns related with the appearance of different pharmacological profiles including antimicrobial activity. Until now, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. In the present study, we develop by the first time, a unified multitasking (mtk) QSAR model for the simultaneous prediction of anti-streptococci activity and toxic effects against biological models like Mus musculus and Rattus norvegicus. The mtk-QSAR model was created by using artificial neural networks (ANN) analysis for the classification of compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under diverse sets of experimental conditions. Our mtk-QSAR model, correctly classified more than 97% of the cases in the whole database (more than 11,500 cases), serving as a promising tool for the virtual screening of potent and safe anti-streptococci drugs.

Discovery of MAO-B Inhibitors - Present Status and Future Directions Part I: Oxygen Heterocycles and Analogs

Parkinsons disease (PD) is one of the most common neurodegenerative disorders. The role of monoamine oxidase (MAO) inhibitors has expanded in the PD treatment. The present review will summarize the current structureactivity relationship information available on MAOs inhibitors of unrelated families of compounds of oxygen heterocyclic type based on coumarin, chromone and chalcone scaffolds. As the current hitting-one-target therapeutic strategy has been proved to be quite inefficient in PD, this review will also discuss about the development of multi-target drugs, in which MAO inhibition plays a counter-part, as a novel and promising treatment approach for PD.

Unified Multi-target Approach for the Rational in silico Design of Anti-bladder Cancer Agents

Bladder cancer (BLC) is a very dangerous and common disease which is characterized by an uncontrolled growth of the urinary bladder cells. In the field of chemotherapy, many compounds have been synthesized and evaluated as anti-BLC agents. The future design of more potent anti-BLC drugs depends on a rigorous and rational discovery, where the computer-aided design (CADD) methodologies should play a very important role. However, until now, there is no CADD methodology able to predict anti-BLC activity of compounds versus different BLC cell lines. We report in this work the first unified approach by exploring Quantitative- Structure Activity Relationship (QSAR) studies using a large and heterogeneous database of compounds. Here, we constructed two multi-target (mt) QSAR models for the classification of compounds as anti-BLC agents against four BLC cell lines. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while the second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. We also extracted different substructural patterns which could be responsible for the activity/inactivity of molecules against BLC and we suggested new molecular entities as possible potent and versatile anti-BLC agents.

Role of Ligand-Based Drug Design Methodologies toward the Discovery of New Anti- Alzheimer Agents: Futures Perspectives in Fragment-Based Ligand Design

Alzheimers disease (AD), a degenerative disease affecting the brain, is the single most common source of dementia in adults. The cause and the progression of AD still remains a mystery among medical experts. As a result, a cure has not yet been discovered, even after decades worth of research that started since 1906, when the disease was first identified. Despite the efforts of the scientific community, several of the biological receptors associated with AD have not been sufficiently studied to date, limiting in turn the design of new and more potent anti-AD agents. Thus, the search for new drug candidates as inhibitors of different targets associated with AD constitutes an essential part towards the discovery of new and more efficient anti-AD therapies. The present work is focused on the role of the Ligand-Based Drug Design (LBDD) methodologies which have been applied for the elucidation of new molecular entities with high inhibitory activity against targets related with AD. Particular emphasis is given also to the current state of fragment-based ligand approaches as alternatives of the Fragment-Based Drug Discovery (FBDD) methodologies. Finally, several guidelines are offered to show how the use of fragment-based descriptors can be determinant for the design of multi-target inhibitors of proteins associated with AD.

Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening.

Escherichia coli remains one of the principal pathogens that cause nosocomial infections, medical conditions that are increasingly common in healthcare facilities. E. coli is intrinsically resistant to many antibiotics, and multidrug-resistant strains have emerged recently. Chemoinformatics has been a great ally of experimental methodologies such as high-throughput screening, playing an important role in the discovery of effective antibacterial agents. However, there is no approach that can design safer anti-E. coli agents, because of the multifactorial nature and complexity of bacterial diseases and the lack of desirable ADMET (absorption, distribution, metabolism, elimination, and toxicity) profiles as a major cause of disapproval of drugs. In this work, we introduce the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous virtual prediction of anti-E. coli activities and ADMET properties of drugs and/or chemicals under many experimental conditions. The mtk-QSBER model was developed from a large and heterogeneous data set of more than 37800 cases, exhibiting overall accuracies of >95% in both training and prediction (validation) sets. The utility of our mtk-QSBER model was demonstrated by performing virtual prediction of properties for the investigational drug avarofloxacin (AVX) under 260 different experimental conditions. Results converged with the experimental evidence, confirming the remarkable anti-E. coli activities and safety of AVX. Predictions also showed that our mtk-QSBER model can be a promising computational tool for virtual screening of desirable anti-E. coli agents, and this chemoinformatic approach could be extended to the search for safer drugs with defined pharmacological activities.

Simultaneous modeling of antimycobacterial activities and ADMET profiles: a chemoinformatic approach to medicinal chemistry.

Mycobacteria represent a group of pathogens which cause serious diseases in mammals, including the lethal tuberculosis (Mycobacterium tuberculosis). Despite the mortality of this community-acquired and nosocomial disease mentioned above, other mycobacteria may cause similar infections, acting as dangerous opportunistic pathogens. Additionally, resistant strains belonging to Mycobacterium spp. have emerged. Thus, the design of novel antimycobacterial agents is a challenge for the scientific community. In this sense, chemoinformatics has played a vital role in drug discovery, helping to rationalize chemical synthesis, as well as the evaluation of pharmacological and ADMET (absorption, distribution, metabolism, excretion, toxicity) profiles in both medicinal and pharmaceutical chemistry. Until now, there is no in silico methodology able to assess antimycobacterial activity and ADMET properties at the same time. This work introduces the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous prediction of antimycobacterial activities and ADMET profiles of drugs/chemicals under diverse experimental conditions. The mtk-QSBER model was constructed by using a large and heterogeneous dataset of compounds (more than 34600 cases), displaying accuracies higher than 90% in both, training and prediction sets. To illustrate the utility of the present model, several molecular fragments were selected and their contributions to different biological effects were calculated and analyzed. Also, many properties of the investigational drug TMC-207 were predicted. Results confirmed that, from one side, TMC-207 can be a promising antimycobacterial drug, and on the other hand, this study demonstrates that the present mtk-QSBER model can be used for virtual screening of safer antimycobacterial agents.

Chemoinformatics for medicinal chemistry: in silico model to enable the discovery of potent and safer anti-cocci agents

BACKGROUND Gram-positive cocci are increasingly antibiotic-resistant bacteria responsible for causing serious diseases. Chemoinformatics can help to rationalize the discovery of more potent and safer antibacterial drugs. We have developed a chemoinformatic model for simultaneous prediction of anti-cocci activities, and profiles involving absorption, distribution, metabolism, elimination and toxicity (ADMET). RESULTS A dataset containing 48,874 cases from many different chemicals assayed under dissimilar experimental conditions was created. The best model displayed accuracies around 93% in both training and prediction (test) sets. Quantitative contributions of several fragments to the biological effects were calculated and analyzed. Multiple biological effects of the investigational drug JNJ-Q2 were correctly predicted. CONCLUSION Our chemoinformatic model can be used as powerful tool for virtual screening of promising anti-cocci agents.

Multitasking models for quantitative structure–biological effect relationships: current status and future perspectives to speed up drug discovery

Introduction: Drug discovery is the process of designing new candidate medications for the treatment of diseases. Over many years, drugs have been identified serendipitously. Nowadays, chemoinformatics has emerged as a great ally, helping to rationalize drug discovery. In this sense, quantitative structure–activity relationships (QSAR) models have become complementary tools, permitting the efficient virtual screening for a diverse number of pharmacological profiles. Despite the applications of current QSAR models in the search for new drug candidates, many aspects remain unresolved. To date, classical QSAR models are able to predict only one type of biological effect (activity, toxicity, etc.) against only one type of generic target. Areas covered: The present review discusses innovative and evolved QSAR models, which are focused on multitasking quantitative structure–biological effect relationships (mtk-QSBER). Such models can integrate multiple kinds of chemical and biological data, allowing the simultaneous prediction of pharmacological activities, toxicities and/or other safety profiles. Expert opinion: The authors strongly believe, given the potential of mtk-QSBER models to simultaneously predict the dissimilar biological effects of chemicals, that they have much value as in silico tools for drug discovery. Indeed, these models can speed up the search for efficacious drugs in a number of areas, including fragment-based drug discovery and drug repurposing.

Computer-aided drug design methodologies toward the design of anti-hepatitis C agents.

Hepatitis C constitutes an infectious disease that causes severe damages to the liver, and is caused by hepatitis C virus. There is no vaccine against this type of disease and the number of people infected continues to grow worldwide. The anti-viral therapy which is currently used is a mixture of interferon alpha-2a with ribavirin, but approximately half of the patients do not respond to therapy. Therefore, it is necessary to search for new compounds with anti-hepatitis C activity. Computer-aided drug design methodologies have been vital in the discovery of candidates to drugs. This review is dedicated to the role of computer-aided drug design methodologies for the development of new anti-hepatitis C agents. In addition, we introduce a QSAR model based on substructural approaches in order to model the anti-hepatitis C activity in vivo.