Synergistic Effects of Caffeine in Combination with Conventional Drugs: Perspectives of a Drug That Never Ages

Plants have been known since ancient times for their healing properties, being used as preparations against human diseases of different etiologies. More recently, natural products have been studied and characterized, isolating the phytochemicals responsible for their bioactivity. Most certainly, there are currently numerous active compounds extracted from plants and used as drugs, dietary supplements, or sources of bioactive molecules that are useful in modern drug discovery. Furthermore, phytotherapeutics can modulate the clinical effects of co-administered conventional drugs. In the last few decades, the interest has increased even more in studying the positive synergistic effects between plant-derived bioactives and conventional drugs. Indeed, synergism is a process where multiple compounds act together to exert a merged effect that is greater than that of each of them summed together. The synergistic effects between phytotherapeutics and conventional drugs have been described in different therapeutic areas, and many drugs are based on synergistic interactions with plant derivatives. Among them, caffeine has shown positive synergistic effects with different conventional drugs. Indeed, in addition to their multiple pharmacological activities, a growing body of evidence highlights the synergistic effects of caffeine with different conventional drugs in various therapeutic fields. This review aims to provide an overview of the synergistic therapeutic effects of caffeine and conventional drugs, summarizing the progress reported to date

Design, synthesis and biological evaluation of new pyrimidine derivatives as anticancer agents

BACKGROUND: Anticancer drug resistance is a challenging phenomenon of growing concern which arises from alteration in drug targets. Despite the fast speed of new chemotherapeutic agent design, the increasing prevalence of this phenomenon requires further research and treatment development. Recently, we reported a new aminopyrimidine compound-namely RDS 344-as a potential innovative anticancer agent.METHODS: Herein, we report the design, synthesis, and anti-proliferative activity of new aminopyrimidine derivatives structurally related to RDS 3442 obtained by carrying out substitutions at position 6 of the pyrimidine core and/or on the 2-aniline ring of our hit. The ability to inhibit cell proliferation was evaluated on different types of tumors, glioblastoma, triple-negative breast cancer, oral squamous cell carcinomas and colon cancer plus on human dermal fibroblasts chosen as control of normal cells.RESULTS: The most interesting compound was the N-benzyl counterpart of RDS 3442, namely 2a, that induced a significant decrease in cell viability in all the tested tumor cell lines, with EC50s ranging from 4 and 8 muM, 4-13 times more active of hit.CONCLUSIONS: These data suggest a potential role for this class of molecules as promising tool for new approaches in treating cancers of different histotype

Analytical characterization of an inulin-type fructooligosaccharide from root-tubers of Asphodelus ramosus L

Plant-based systems continue to play a pivotal role in healthcare, and their use has been extensively documented. Asphodelus L. is a genus comprising various herbaceous species, known by the trivial name Asphodelus. These plants have been known since antiquity for both food and therapeutic uses, especially for treating several diseases associated with inflammatory and infectious skin disorders. Phytochemical studies revealed the presence of different constituents, mainly anthraquinones, triterpenoids, phenolic acids, and flavonoids. Although extensive literature has been published on these constituents, a paucity of information has been reported regarding the carbohydrate composition, such as fructans and fructan-like derivatives. The extraction of watersoluble neutral polysaccharides is commonly performed using water extraction, at times assisted by microwaves and ultrasounds. Herein, we reported the investigation of the alkaline extraction of roottubers of Asphodelus ramosus L., analyzing the water-soluble polysaccharides obtained by precipitation from the alkaline extract and its subsequent purification by chromatography. A polysaccharide was isolated by alkaline extraction; the HPTLC study to determine its composition showed fructose as the main monosaccharide. FT-IR analysis showed the presence of an inulin-type structure, and NMR analyses allowed us to conclude that A. ramosus roots contain polysaccharide with an inulin-type fructooligosaccharide with a degree of polymerization of 7-8

Investigation of Commiphora myrrha (Nees) Engl. oil and its main components for antiviral activity

The resinous exudate produced by Commiphora myrrha (Nees) Engl. is commonly known as true myrrh and has been used since antiquity for several medicinal applications. Hundreds of metabolites have been identified in the volatile component of myrrh so far, mainly sesquiterpenes. Although several efforts have been devoted to identifying these sesquiterpenes, the phytochemical analyses have been performed by gas-chromatography/mass spectrometry (GC–MS) where the high temperature employed can promote degradation of the components. In this work, we report the extraction of C. myrrha by supercritical CO2, an extraction method known for the mild extraction conditions that allow avoiding undesired chemical reactions during the process. In addition, the analyses of myrrh oil and of its metabolites were performed by HPLC and GC–MS. Moreover, we evaluated the antiviral activity against influenza A virus of the myrrh extracts, that was possible to appreciate after the addition of vitamin E acetate (α-tocopheryl acetate) to the extract. Further, the single main bioactive components of the oil of C. myrrha commercially available were tested. Interestingly, we found that both furanodienone and curzerene affect viral replication by acting on different steps of the virus life cycle

Diketo acid inhibitors of nsp13 of SARS-CoV-2 block viral replication

For RNA viruses, RNA helicases have long been recognized to play critical roles during virus replication cycles, facilitating proper folding and replication of viral RNAs, therefore representing an ideal target for drug discovery. SARS-CoV-2 helicase, the non-structural protein 13 (nsp13) is a highly conserved protein among all known coronaviruses, and, at the moment, is one of the most explored viral targets to identify new possible antiviral agents. In the present study, we present six diketo acids (DKAs) as nsp13 inhibitors able to block both SARS-CoV-2 nsp13 enzymatic functions. Among them four compounds were able to inhibit viral replication in the low micromolar range, being active also on other human coronaviruses such as HCoV229E and MERS CoV. The experimental investigation of the binding mode revealed ATP-non-competitive kinetics of inhibition, not affected by substrate-displacement effect, suggesting an allosteric binding mode that was further supported by molecular modelling calculations predicting the binding into an allosteric conserved site located in the RecA2 domain

Miconazole-like scaffold is a promising lead for Naegleria fowleri-specific CYP51 inhibitors

Developing drugs for brain infection by Naegleria fowleri is an unmet medical need. We used a combination of cheminformatics, target-, and phenotypic-based drug discovery methods to identify inhibitors that target an essential N. fowleri enzyme, sterol 14-demethylase (NfCYP51). A total of 124 compounds preselected in silico were tested against N. fowleri. Nine primary hits with EC50 ≤ 10 μM were phenotypically identified. Cocrystallization with NfCYP51 focused attention on one primary hit, miconazole-like compound 2a. The S-enantiomer of 2a produced a 1.74 Å cocrystal structure. A set of analogues was then synthesized and evaluated to confirm the superiority of the S-configuration over the R-configuration and the advantage of an ether linkage over an ester linkage. The two compounds, S-8b and S-9b, had an improved EC50 and KD compared to 2a. Importantly, both were readily taken up into the brain. The brain-to-plasma distribution coefficient of S-9b was 1.02 ± 0.12, suggesting further evaluation as a lead for primary amoebic meningoencephalitis

New Thiazolidine-4-One Derivatives as SARS-CoV-2 Main Protease Inhibitors

It has been more than four years since the first report of SARS-CoV-2, and humankind has experienced a pandemic with an unprecedented impact. Moreover, the new variants have made the situation even worse. Among viral enzymes, the SARS-CoV-2 main protease (Mpro) has been deemed a promising drug target vs. COVID-19. Indeed, Mpro is a pivotal enzyme for viral replication, and it is highly conserved within coronaviruses. It showed a high extent of conservation of the protease residues essential to the enzymatic activity, emphasizing its potential as a drug target to develop wide-spectrum antiviral agents effective not only vs. SARS-CoV-2 variants but also against other coronaviruses. Even though the FDA-approved drug nirmatrelvir, a Mpro inhibitor, has boosted the antiviral therapy for the treatment of COVID-19, the drug shows several drawbacks that hinder its clinical application. Herein, we report the synthesis of new thiazolidine-4-one derivatives endowed with inhibitory potencies in the micromolar range against SARS-CoV-2 Mpro. In silico studies shed light on the key structural requirements responsible for binding to highly conserved enzymatic residues, showing that the thiazolidinone core acts as a mimetic of the Gln amino acid of the natural substrate and the central role of the nitro-substituted aromatic portion in establishing π-π stacking interactions with the catalytic His-41 residue

Variational Inference in Dynamical Systems

Dynamical systems are a powerful formalism to analyse the world around us. Many datasets are sequential in nature, and can be described by a discrete time evolution law. We are interested in approaching the analysis of such datasets from a probabilistic perspective. We would like to maintain justified beliefs about quantities which, though useful in explaining the behaviour of a system, may not be observable, as well as about the system's evolution itself, especially in regimes we have not yet observed in our data. The framework of statistical inference gives us the tools to do so, yet, for many systems of interest, performing inference exactly is not computationally or analytically tractable. The contribution of this thesis, then, is twofold: first, we uncover two sources of bias in existing variational inference methods applied to dynamical systems in general, and state space models whose transition function is drawn from a Gaussian process (GPSSM) in particular. We show bias can derive from assuming posteriors in non-linear systems to be jointly Gaussian, and from assuming that we can sever the dependence between latent states and transition function in state space model posteriors. Second, we propose methods to address these issues, undoing the resulting biases. We do this without compromising on computational efficiency or on the ability to scale to larger datasets and higher dimensions, compared to the methods we rectify. One method, the Markov Autoregressive Flow (Markov AF) addresses the Gaussian assumption, by providing a more flexible class of posteriors, based on normalizing flows, which can be easily evaluated, sampled, and optimised. The other method, Variationally Coupled Dynamics and Trajectories (VCDT), tackles the factorisation assumption, leveraging sparse Gaussian processes and their variational representation to reintroduce dependence between latent states and the transition function at no extra computational cost. Since the objective of inference is to maintain calibrated beliefs, if we employed approximations which are significantly biased in non-linear, noisy systems, or when there is little data available, we would have failed in our objective, as those are precisely the regimes in which uncertainty quantification is all the more important. Hence we think it is essential, if we wish to act optimally on such beliefs, to uncover, and, if possible, to correct, all sources of systematic bias in our inference methods.Qualcomm Innovation Fun