Introduction:The COVID-19 pandemic resulted in disastrous human and economic costs, mainly due to the initial lack of specific treatments. Complementary to immunotherapies, drug repurposing is possibly the best option to arrive at COVID-19 treatments in the short term. Areas covered: Repurposing prospects undergoing clinical trials or with some level of evidence emerging from clinical studies are overviewed. The authors discuss some possible intellectual property and commercial barriers to drug repurposing, and strategies to facilitate equitable access to incoming therapeutic solutions, highlighting the importance of collaborative drug discovery models. Based on a critical analysis of the available literature about in silico screens against SARS-CoV-2 main protease, the authors illustrate how frequently overconfident conclusions are being drawn in COVID-19-related literature. Expert opinion: Most of the current clinical trials on potential COVID-19 treatments are, in fact, drug repurposing examples. In October 2020, the FDA approved a repurposed antiviral, remdesivir, as the first treatment for COVID-19. Considering the high expectations invested in approaching therapeutic solutions, the scientific community must be careful not to raise unrealistic expectations. Today more than ever, the conclusions drawn in scientific reports have to be fully supported by the level of evidence, avoiding any sort of unfounded speculation.

The scientific community is working against the clock to arrive at therapeutic interventions to treat patients with COVID-19. Among the strategies for drug discovery, virtual screening approaches have the capacity to search potential hits within millions of chemical structures in days, with the appropriate computing infrastructure. In this article, we first analyzed the published research targeting the inhibition of the main protease (Mpro), one of the most studied targets of SARS-CoV-2, by docking-based methods. An alarming finding was the lack of an adequate validation of the docking protocols (i.e., pose prediction and virtual screening accuracy) before applying them in virtual screening campaigns. The performance of the docking protocols was tested at some level in 57.7% of the 168 investigations analyzed. However, we found only three examples of a complete retrospective analysis of the scoring functions to quantify the virtual screening accuracy of the methods. Moreover, only two publications reported some experimental evaluation of the proposed hits until preparing this manuscript. All of these findings led us to carry out a retrospective performance validation of three different docking protocols, through the analysis of their pose prediction and screening accuracy. Surprisingly, we found that even though all tested docking protocols have a good pose prediction, their screening accuracy is quite limited as they fail to correctly rank a test set of compounds. These results highlight the importance of conducting an adequate validation of the docking protocols before carrying out virtual screening campaigns, and to experimentally confirm the predictions made by the models before drawing bold conclusions. Finally, successful structure-based drug discovery investigations published during the redaction of this manuscript allow us to propose the inclusion of target flexibility and consensus scoring as alternatives to improve the accuracy of the methods.

The great efforts of many researchers have brought down some of the barriers that exist to turn a good in vitro compound into a potential in vivo drug. The advent of pharmaceutical nanotechnology has allowed an arsenal of drugs with poor stability, low solubility, high off-target toxicity and other disadvantageous features, to be accessible as pharmaceutical products that could be administered to a patient. Nanotechnology was introduced in drug delivery very long ago, but has flourished with unprecedented intensity during the last twenty years and now a diversity of nano-based preparations are at clinical stage of development or already available in the market. Undoubtedly, nanotechnology plays a key role in future pharmaceutical development and pharmacotherapy. In the first part of this review, we have already discussed recent (2008-2012) patents on linear polymer-based nanosystems (nanogels, nanospheres and nanocapsules) applications to cancer therapy. Here, we have expanded such analysis to branched polymers (dendrimers), self-assembling nanomicelles and lipid-based nanocarriers.

CNS drug development is characterized by an especially high attrition rate, despite clear unmet medical needs in the field of neuro-pharmacology and significant investment in R of novel CNS drug treatments. Here, we overview the issues underlying the intrinsic difficulty of CNS drugs development, including obstacles of pharmacokinetic nature and lack of predictivity of preclinical tests. We highlight current efforts to overcome these limitations, with an emphasis on modeling opportunities towards early recognition of CNS candidates (stressing the possibilities of multi-target directed ligands or "magic shotguns") and different approaches to improve CNS bioavailability.

One of the greatest challenges in cancer drug therapy is to maximize the effectiveness of the active agent while reducing its systemic adverse effects. To add more, many widely-used chemoterapeutic agents present unfavorable physicochemical properties (e.g. low solubility, lack of chemical or biological stability) that hamper or limit their therapeutic applications. All these issues may be overcome by designing adequate drug delivery systems; nanocarriers are particularly suitable for this purpose. Nanosystems can be used for targeted-drug release, treatment, diagnostic imaging and therapy monitoring. They allow the formulation of drug delivery systems with user-defined characteristics regarding solubility, biodegradability, particle size, release kinetics and active targeting, among others. This review (Part I) focuses on recent patents published between 2008 and the present day, related to nanospheres, nanocapsules and nanogels applied to anticancer drug therapy. Other nanosystems is covered in a second article (Part II).

ABC efflux transporters are polyspecific members of the ABC superfamily that, acting as drug and metabolite carriers, provide a biochemical barrier against drug penetration and contribute to detoxification. Their overexpression is linked to multidrug resistance issues in a diversity of diseases. Breast cancer resistance protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting oral absorption and brain bioavailability of its substrates. Early recognition of BCRP substrates is thus essential to optimize oral drug absorption, design of novel therapeutics for central nervous system conditions, and overcome BCRP-mediated cross-resistance issues. We present the development of an ensemble of ligand-based machine learning algorithms for the early recognition of BCRP substrates, from a database of 262 substrates and nonsubstrates compiled from the literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models were developed through application of linear discriminant analysis to random subsamples of Dragon molecular descriptors. Simple data fusion and statistical comparison of partial areas under the curve of ROC curves were applied to obtain the best 2-model combination, which presented 82% and 74.5% of overall accuracy in the training and test set, respectively.

Breast Cancer Resistance Protein (BCRP) is an ATP-dependent efflux transporter linked to the multidrug resistance phenomenon in many diseases such as epilepsy and cancer and a potential source of drug interactions. For these reasons, the early identification of substrates and nonsubstrates of this transporter during the drug discovery stage is of great interest. We have developed a computational nonlinear model ensemble based on conformational independent molecular descriptors using a combined strategy of genetic algorithms, J48 decision tree classifiers, and data fusion. The best model ensemble consists in averaging the ranking of the 12 decision trees that showed the best performance on the training set, which also demonstrated a good performance for the test set. It was experimentally validated using the ex vivo everted rat intestinal sac model. Five anticonvulsant drugs classified as nonsubstrates for BRCP by the model ensemble were experimentally evaluated, and none of them proved to be a BCRP substrate under the experimental conditions used, thus confirming the predictive ability of the model ensemble. The model ensemble reported here is a potentially valuable tool to be used as an in silico ADME filter in computer-aided drug discovery campaigns intended to overcome BCRP-mediated multidrug resistance issues and to prevent drug-drug interactions.

Despite the introduction of more than 15 third generation antiepileptic drugs to the market from 1990 to the moment, about one third of the epileptic patients still suffer from refractory to intractable epilepsy. Several hypotheses seek to explain the failure of drug treatments to control epilepsy symptoms in such patients. The most studied one proposes that drug resistance might be related with regional overactivity of efflux transporters from the ATP-Binding Cassette (ABC) superfamily at the blood-brain barrier and/or the epileptic foci in the brain. Different strategies have been conceived to address the transporter hypothesis, among them inhibiting or down-regulating the efflux transporters or bypassing them through a diversity of artifices. Here, we review scientific evidence supporting the transporter hypothesis along with its limitations, as well as computer-assisted early recognition of ABC transporter substrates as an interesting strategy to develop novel antiepileptic drugs capable of treating refractory epilepsy linked to ABC transporters overactivity.