Current Pharmaceutical Design - Volume 22, Issue 46, 2016

Single target based approaches often proved to be unsuccessful in complex multigenic diseases such as cancer or schizophrenia. Multi-target drugs can be more efficacious in this regard by modulating multiple processes in the organism. According to the theory of polypharmacology, bioactive molecules possess characteristic interaction patterns that are responsible for their effects and side-effects and getting acquainted with this typical profile is increasingly desired to promote pharmaceutical research and development. There is a novel way of approaching polypharmacology that takes into account the interaction of molecules to a set of proteins that are not necessarily known biological targets of the compounds. Applying a carefully selected panel of proteins that can model the possible interactions a molecule can exert when administered to a human body, holds out a promise of biological activity prediction. This review aims to summarize a number of such bioactivity profiling-based approaches set up recently and present their application areas within the drug discovery field.

For many years, the “one target, one drug” paradigm has been the driving force behind developments in pharmaceutical research. With the recent advances in molecular biology and genomics technologies, the focus is shifting toward “drug-holistic” systems based approaches (i.e. systems pharmacology). The integration of large and diverse amount of data from chemistry and biology coupled with the development and the application of network-based approaches to cope with these data is the next paradigm of drug discovery. Systems pharmacology offers a novel way of approaching drug discovery by developing models that consider the global physiological environment of protein targets and their modification by drugs. Studying drug action across multiple scales of complexity from molecular and cellular to tissue and organism levels may help identify new druggable disease genes and to design new drugs with a better efficacy and clinical safety.

Current computational and mathematical tools are demonstrating the high value of using systems modeling approaches (e.g. Quantitative Systems Pharmacology) to understand the effect of a given compound on the biological and physiological mechanisms related to a specific disease. This review provides a short survey of the evolution of the mathematical approaches used to understand the effect of particular cholesterol-lowering drugs, from pharmaco-kinetic (PK) / pharmaco-dynamic (PD) models, through physiologically base pharmacokinetic models (PBPK) to QSP. These mathematical models introduce more mechanistic information related to the effect of these drugs on atherosclerosis progression and demonstrate how QSP could open new ways for stratified medicine in this field.

Drug discovery is complex and expensive. Numerous drug candidates fail late in clinical trials or even after being released to the market. These failures are not only due to commercial considerations and less optimal drug efficacies but, adverse reactions originating from toxic effects also constitute a major challenge. During the last two decades, significant advances have been made enabling the early prediction of toxic effects using in silico techniques. However, by design, these essentially statistical techniques have not taken the disease driving pathophysiological mechanisms into account. The complexity of such mechanisms in combination with their interactions with drugspecific properties and environmental and life-style related factors renders the task of predicting toxicity on a purely statistical basis which is an insurmountable challenge. In response to this situation, an interdisciplinary field has developed, referred to as systems toxicology, where the notion of a network is used to integrate and model different types of information to better predict drug toxicity. In this study, we briefly review the merits and limitations of such recent promising predictive approaches integrating molecular networks, chemical compound networks, and protein drug association networks.

Cancer cell line panels have proved useful disease models to, among others, identify genomic markers of drug sensitivity and to develop new anticancer drugs. The increasing availability of in vitro sensitivity and cell line profiling data sets raises the question of whether this information could be used, and to which extent, to predict the activity of drugs in cancer cell lines and, ultimately, in patients tumors. Drug sensitivity prediction embraces those approaches aiming at predicting in vitro drug activity on cancer cell lines by integrating genomic and/or chemical information using machine learning models. In this review, we summarize the cytotoxicity assays generally used to determine in vitro activity on cultured cell lines, and revisit the drug sensitivity prediction studies that have leveraged chemical and cell line profiling data from the NCI60, Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) projects. A section outlining current limitations and future perspectives in the field closes the review.

Treatment of ocular disorders is a challenge due to the difficulty of delivering drugs to the target tissues within the eye at sufficient concentrations to produce a therapeutic effect. The cornea and the blood-retinal barrier, comprising of the retinal pigment epithelium and the retinal capillaries, are the main barriers for delivering drugs to treat diseases in the anterior and posterior parts of the eye, respectively. The eye has a rich blood supply and relatively small mass, and drugs can distribute from the systemic blood circulation to the choroid through the fenestrated choroidal blood vessels, but further permeation into the eye is limited by the blood-retinal barrier. Computational prediction of the ocular pharmacokinetics of drugs can help improve drug delivery and predict ocular adverse effects resulting from ocular or systemic drugs. Computational models predicting ocular adverse effects of drugs are still scarce, even though prediction of eye irritation and corrosion of chemicals has been well studied as a consequence of recent European Union legislation. Predictive modeling of adverse effects suffers from the wide distribution of data resources, but databases that integrate data on adverse effects, drugs, targets and other related biological data from different sources offer improved prospects for predictive modeling.

Over the past decade, a more comprehensive, large-scale approach to studying cancer genetics and biology has revealed the challenges of tumor heterogeneity, adaption, evolution and drug resistance, while systems-based pharmacology and chemical biology strategies have uncovered a much more complex interaction between drugs and the human proteome than was previously anticipated. In this mini-review we assess the progress and potential of drug polypharmacology in biomarker-driven precision oncology. Polypharmacology not only provides great opportunities for drug repurposing to exploit off-target effects in a new single-target indication but through simultaneous blockade of multiple targets or pathways offers exciting opportunities to slow, overcome or even prevent inherent or adaptive drug resistance. We highlight the many challenges associated with exploiting known or desired polypharmacology in drug design and development, and assess computational and experimental methods to uncover unknown polypharmacology. A comprehensive understanding of the intricate links between polypharmacology, efficacy and safety is urgently needed if we are to tackle the enduring challenge of cancer drug resistance and to fully exploit polypharmacology for the ultimate benefit of cancer patients.

Breast cancer is the most frequent cancer in women. Despite advances in early detection and treatment, it has the second highest mortality rate after lung cancer. Around 85% of breast carcinomas are ER+; thus, antiestrogens like tamoxifen are beneficial. Although, tamoxifen is useful for many patients, a number of patients respond poorly to initial therapy or recurrence occurs in about 30% of cases, because tamoxifen resistance happens. Drug resistance remains a major clinical obstacle to successful treatment of breast cancer and more than 90% of unsuccessful treatments are because of acquired resistance and MultiDrug Resistance (MDR) is a major contributor. MicroRNAs are members of a novel class of short noncoding RNAs. Besides their various roles in gene expression, miRNAs are considered as important cancer therapeutic targets and biomarkers. Since 2005, when miRNA deregulation was first reported in breast cancer, more than 1000 reports have been published about miRNAs. Increasing number of studies showed the importance of miRNAs in antiestrogen therapy, especially on tamoxifen; thus, it is not surprising that these tiny molecules are involved in drug resistance. Due to the pivotal role of these known RNA molecules, in this review, we tried to illustrate the importance of the miRNAs as a new player in breast cancer pathogenesis. We have also focused on cancer drug resistance mechanisms highlighting the role of important oncomirs, miR 221/222, involved in cell cycle deregulation in breast cancer. The relationship between these oncomiRs with resistance to tamoxifen is also emphasized.

The combined use of a photosensitizing agent, light and dioxygen for the treatment of diseases has become known as photodynamic therapy (PDT) and was first discovered more than one hundred years ago. Over the years, PDT has proven its potential for the treatment of malignant and non-malignant lesions in addition to classical cancer therapy in numerous clinical studies, but application as a routine method is still limited. In this review, the development of modern PDT since the beginning of the twentieth century is briefly portrayed. The underlying mechanisms of phototoxicity are explained, and the requirements for ideal photosensitizers (PS) are underlined. Selected PS are introduced and examples of some of the many attempts made at the optimization of PDT by developing new PS with improved chemical and phototoxic properties, are reviewed. Promising 3rd generation PS are introduced and newer approaches to increasing tumor selectivity and efficacy of PDT, such as with nanoparticles, are discussed.

Background: This work aimed to evaluate the effects of genistein treatment in Collagen Induced Arthritis (CIA) mouse model. Methods: CIA was elicited in DBA/1 Mice by an intradermal injection of 100 μL of an emulsion of bovine type II collagen (CII) in isovolumic incomplete Freund’s adjuvant (IFA) at the base of the tail. Twenty-one days later, a second injection of CII in IFA was administered at the base of the tail. After the symptoms of arthritis showed in mouse model, we divided animals into two groups according to their clinical symptom scores. The treatment group was intraperitoneally injected daily with genistein (based on the pre-experiment data and literature reported, 5 mg/kg dose was selected and tested) for 12 days, while the control group was injected with phosphate buffered saline. Inflammatory cytokines titer, radiological, and histological observations were completed at different time’s points after treatment. CT analysis was conducted 3 months after the treatment to observe the articular structures. Immunohistochemical analysis was performed to investigate the expression and distribution of VEGF in joint tissues. Results: Genistein suppressed the expressions of IL-1β, IL-6 and TNF-α in the serum. Radiological results showed that bone degradation was inhibited by the treatment. Moreover, hematoxylin and eosin staining showed that the degree of inflammation was relieved. In the cartilage area, TRAP stain-positive cells were detected, which was notably reduced in the treatment group compared to the control group. Micro-CT 3D images clearly exhibited that the joint adhered and structures destroyed in the control group with less destruction in the treatment group. Furthermore, genistein suppressed VEGF expression, and blocked angiogenesis in the synovial tissue. Conclusion: Our work provides further data regarding the effects of genistein as a potential treatment drug for RA, as well as the role of genistein in the anti-inflammatory pathway in RA therapy.

Earlier we reported the identification of diarylpyrimidine-quinolone hybrids as a new class of HIV-1 NNRTIs. A few of these hybrids displayed moderate inhibitory activity against wt HIV-1 replication at submicromolar level, however, all of them lacked inhibitory activity against the double mutant virus (K103N/Y181C), which is the most prevalent NNRTI resistant-associated double mutant observed in the clinic. In the present study, we designed and synthesized a new series of diarylpyrimidine-quinolone hybrids featuring a halogen group at C-6'; position of quinolone ring. The biological results indicated that most of these hybrids could inhibit wt HIV-1 replication at nanomolar level ranging from 0.088 to 0.0096 μM. The most promising hybrid 5c displayed a significant EC50 value of 0.0096 μM against HIV-1 IIIB and of 0.98 μM against K103N/Y181C. Further docking studies revealed that these hybrids could be well located in the hydrophobic NNIBP of HIV-1 RT despite the bulky and polar properties of a quinolone 3-carboxylic acid scaffold in the molecules. These promising results suggested a high potential to further develop these hybrids as next-generation NNRTIs with improved antiviral efficacy and resistance profile.

Background: Cyclodextrin-based nanosponges are hyper-cross-linked supramolecular architectures. Similar to the parent compound they are derived from, they have been shown to possess the ability to form inclusion and non-inclusion complexes with drugs of different polarities, enzymes, proteins, peptides and macromolecules. Cyclodextrins only form inclusion complexes with hydrophobic guest molecules, while nanosponges extend this function by forming inclusion and non-inclusion complexes with both hydrophobic and hydrophilic compounds. This host-guest interaction provides enhancement in stability, solubility and sometimes in membrane permeability. Objective: The potential of ester-based hydrophilic nanosponges as drug carriers in topical ocular drug delivery was assessed by undertaking drug loading, drug release, phase solubility and ex vivo pig corneal drug permeation studies on the nanosponges. Method: The ester-based cyclodextrin-based nanosponges were synthesized through cross-linking β-cyclodextrin by pyromellitic dianhydride. Various concentrations of pyromellitic dianhydride, the cross-linker, were initially tested for their effects on the drug loading capacity of the nanosponges. Thereafter, varying ratios of drug to nanosponge complexes were studied and analyzed for their drug release kinetics, and solubility and permeation effects. Results: A cyclodextrin-based nanosponge drug delivery system with potential to improve corneal permeation and drug solubility was formulated. Their ability to improve pig corneal permeation was shown; where up to 75% permeation of the model drug was achieved in the presence of the nanosponges as compared to the 28% permeation of the pure drug. The nanosponges provided drug release for approximately an hour. This is a common behavior for hydrophilic complexes due to their high solubility in aqueous media. Conclusion: The ester-based hydrophilic cyclodextrin-based nanosponge derivative can be used as a drug delivery system in topical ocular drug delivery, to improve stability, solubility and corneal permeation.