Cationic Vitamin E-TPGS Mixed Micelles of Berberine to Neutralize Doxorubicin-Induced Cardiotoxicity via Amelioration of Mitochondrial Dysfunction and Impeding Apoptosis.

Anthracycline antibiotics, namely, doxorubicin (DOX) and daunorubicin, are among the most widely used anticancer therapies, yet are notoriously associated with severe myocardial damage due to oxidative stress and mitochondrial damage. Studies have indicated the strong pharmacological properties of Berberine (Brb) alkaloid, predominantly mediated via mitochondrial functions and nuclear networks. Despite the recent emphasis on Brb in clinical cardioprotective studies, pharmaceutical limitations hamper its clinical use. A nanoformulation for Brb was developed (mMic), incorporating a cationic lipid, oleylamine (OA), into the TPGS-mixed corona of PEGylated-phosphatidylethanolamine (PEG-PE) micelles. Cationic TPGS/PEG-PE mMic with superior Brb loading and stability markedly enhanced both intracellular and mitochondria-tropic Brb activities in cardiovascular muscle cells. Sub-lethal doses of Brb via cationic OA/TPGS mMic, as a DOX co-treatment, resulted in significant mitochondrial apoptosis suppression. In combination with an intense DOX challenge (up to ~50 µM), mitochondria-protective Brb-OA/TPGS mMic showed a significant 24 h recovery of cell viability (p ≤ 0.05-0.01). Mechanistically, the significant relative reduction in apoptotic caspase-9 and elevation of antiapoptotic Bcl-2 seem to mediate the cardioprotective role of Brb-OA/TPGS mMic against DOX. Our report aims to demonstrate the great potential of cationic OA/TPGS-mMic to selectively enhance the protective mitohormetic effect of Brb to mitigate DOX cardiotoxicity.

Structural Optimization of Platinum Drugs to Improve the Drug-Loading and Antitumor Efficacy of PLGA Nanoparticles.

Currently, molecular dynamics simulation is being widely applied to predict drug-polymer interaction, and to optimize drug delivery systems. Our study describes a combination of in silico and in vitro approaches aimed at improvement in polymer-based nanoparticle design for cancer treatment. We applied the PASS service to predict the biological activity of novel carboplatin derivatives. Subsequent molecular dynamics simulations revealed the dependence between the drug-polymer binding energy along with encapsulation efficacy, drug release profile, and the derivatives' chemical structure. We applied ICP-MS analysis, the MTT test, and hemolytic activity assay to evaluate drug loading, antitumor activity, and hemocompatibility of the formulated nanoparticles. The drug encapsulation efficacy varied from 0.2% to 1% and correlated with in silico modelling results. The PLGA nanoparticles revealed higher antitumor activity against A549 human non-small-cell lung carcinoma cells compared to non-encapsulated carboplatin derivatives with IC50 values of 1.40-23.20 µM and 7.32-79.30 µM, respectively; the similar cytotoxicity profiles were observed against H69 and MCF-7 cells. The nanoparticles efficiently induced apoptosis in A549 cells. Thus, nanoparticles loaded with novel carboplatin derivatives demonstrated high application potential for anticancer therapy due to their efficacy and high hemocompatibility. Our results demonstrated the combination of in silico and in vitro methods applicability for the optimization of encapsulation and antitumor efficacy in novel drug delivery systems design.

In silico prediction of siRNA ionizable-lipid nanoparticles In vivo efficacy: Machine learning modeling based on formulation and molecular descriptors.

In silico prediction of the in vivo efficacy of siRNA ionizable-lipid nanoparticles is desirable as it can save time and resources dedicated to wet-lab experimentation. This study aims to computationally predict siRNA nanoparticles in vivo efficacy. A data set containing 120 entries was prepared by combining molecular descriptors of the ionizable lipids together with two nanoparticles formulation characteristics. Input descriptor combinations were selected by an evolutionary algorithm. Artificial neural networks, support vector machines and partial least squares regression were used for QSAR modeling. Depending on how the data set is split, two training sets and two external validation sets were prepared. Training and validation sets contained 90 and 30 entries respectively. The results showed the successful predictions of validation set log (siRNA dose) with Rval 2= 0.86-0.89 and 0.75-80 for validation sets one and two, respectively. Artificial neural networks resulted in the best Rval 2 for both validation sets. For predictions that have high bias, improvement of Rval 2 from 0.47 to 0.96 was achieved by selecting the training set lipids lying within the applicability domain. In conclusion, in vivo performance of siRNA nanoparticles was successfully predicted by combining cheminformatics with machine learning techniques.

Synchronizing In Silico, In Vitro, and In Vivo Studies for the Successful Nose to Brain Delivery of an Anticancer Molecule.

Sesamol is a sesame seed constituent with reported activity against many types of cancer. In this work, two types of nanocarriers, solid lipid nanoparticles (SLNs) and polymeric nanoparticles (PNs), were exploited to improve sesamol efficiency against the glioma cancer cell line. The ability of the proposed systems for efficient brain targeting intranasally was also inspected. By the aid of two docking programs, the virtual loading pattern inside these nanocarriers was matched to the real experimental results. Interactions involved in sesamol-carrier binding were also assessed, followed by a discussion of how different scoring functions account for these interactions. The study is an extension of the computer-assisted drug formulation design series, which represents a promising initiative for an upcoming industrial innovation. The results proved the power of combined in silico tools in predicting members with the highest sesamol payload suitable for delivering a sufficient dose to the brain. Among nine carriers, glyceryl monostearate (GMS) and polycaprolactone (PCL) scored the highest sesamol payload practically and computationally. The EE % was 66.09 ± 0.92 and 61.73 ± 0.47 corresponding to a ΔG (binding energy) of -8.85 ± 0.16 and -5.04 ± 0.11, respectively. Dynamic light scattering evidenced the formation of 215.1 ± 7.2 nm and 414.25 ± 1.6 nm nanoparticles, respectively. Both formulations demonstrated an efficient cytotoxic effect and brain-targeting ability compared to the sesamol solution. This was evidenced by low IC50 (38.50 ± 10.37 µM and 27.81 ± 2.76 µM) and high drug targeting efficiency (7.64 ± 1.89-fold and 13.72 ± 4.1-fold) and direct transport percentages (86.12 ± 3.89 and 92.198 ± 2.09) for GMS-SLNs and PCL-PNs, respectively. The results also showed how different formulations, having different compositions and characteristics, could affect the cytotoxic and targeting ability.

Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers.

This review describes different trials to model and predict drug payload in lipid and polymeric nanocarriers. It traces the evolution of the field from the earliest attempts when numerous solubility and Flory-Huggins models were applied, to the emergence of molecular dynamic simulations and docking studies, until the exciting practically successful era of artificial intelligence and machine learning. Going through matching and poorly matching studies with the wet lab-dry lab results, many key aspects were reviewed and addressed in the form of sequential examples that highlighted both cases.

Comparing cefotaxime and ceftriaxone in combating meningitis through nose-to-brain delivery using bio/chemoinformatics tools.

Bio/chemoinformatics tools can be deployed to compare antimicrobial agents aiming to select an efficient nose-to-brain formulation targeting the meningitis disease by utilizing the differences in the main structural, topological and electronic descriptors of the drugs. Cefotaxime and ceftriaxone were compared at the formulation level (by comparing the loading in gelatin and tripalmitin matrices as bases for the formation of nanoparticulate systems), at the biopharmaceutical level (through the interaction with mucin and the P-gp efflux pumps) and at the therapeutic level (through studying the interaction with S. pneumoniae bacterial receptors). GROMACS v4.6.5 software package was used to carry-out all-atom molecular dynamics simulations. Higher affinity of ceftriaxone was observed compared to cefotaxime on the investigated biopharmaceutical and therapeutic macromolecules. Both drugs showed successful docking on mucin, P-gp efflux pump and S. pneumoniae PBP1a and 2b; but ceftriaxone showed higher affinity to the P-gp efflux pump proteins and higher docking on mucin. Ceftriaxone showed less out-of-matrix diffusion and higher entrapment on the gelatin and the tripalmitin matrices. Accordingly, Ceftriaxone gelatin nanospheres or tripalmitin solid lipid nanoparticles may pose a more feasible and efficient nose-to-brain formulation targeting the meningitis disease compared to the cefotaxime counterparts.

Chloroquine and hydroxychloroquine for combating COVID-19: Investigating efficacy and hypothesizing new formulations using Bio/chemoinformatics tools.

Chloroquine (CQ) and hydroxychloroquine (HCQ) are undergoing several clinical trials for evaluating their efficacy and safety as antiviral drugs. Yet, there is still a great debate about their efficacy in combating COVID-19. This study aimed to evaluate the feasibility of intranasal and/or pulmonary administration of CQ/HCQ for COVID-19 using Bio/chemoinformatics tools. We, hereby, hypothesize the success of the intranasal and the pulmonary routes through a gelatin matrix to overcome several challenges related to CQ and HCQ pharmacodynamics and pharmacokinetics properties and to increase their local concentrations at the sites of initial viral entry while minimizing the potential side effects. Molecular docking on the gelatin-simulated matrix demonstrated high loading values and a sustained release profile. Moreover, the docking on mucin as well as various receptors including Angiotensin-converting enzyme 2 (ACE-2), heparin sulphate proteoglycan and Phosphatidylinositol binding clathrin assembly protein (PICALM), which are expressed in the lung and intranasal tissues and represent initial sites of attachment of the viral particles to the surface of respiratory cells, has shown good binding of CQ and HCQ to these receptors. The presented data provide an insight into the use of a novel drug formulation that needs to be tested in adequately powered randomized controlled clinical trials; aiming for a sustained prophylaxis effect and/or a treatment strategy against this pandemic viral infection.

Chitosan-coated nanodiamonds: Mucoadhesive platform for intravesical delivery of doxorubicin.

Nanodiamonds (NDs) are an emerging delivery system with a massive surface area qualifying them for efficient loading with various drugs. However, NDs easily scavenge ions upon mixing with physiological media leading to rapid aggregation. Herein, chitosan was employed to endue steric stabilization to NDs and confer adhesiveness to the particles improving their retention in the urinary bladder. The effect of chitosan molecular weight and pH on the particle size and surface charge of chitosan-coated doxorubicin-loaded NDs (Chi-NDX) was investigated. Selected formula exhibited high drug loading efficiency (>90 %), small particle size (<150 nm), good colloidal stability, acid-favored drug release but limited stability in cell culture media. After further stabilization with TPP or dextran sulfate, selected TPP-treated formula displayed more potent cytotoxic effect compared with free doxorubicin and uncoated nanoparticles, and higher drug retention in ex vivo bovine bladder. Therefore, TPP-Chi-NDX is suggested as a promising system for mucosal anticancer delivery.

Novel thymoquinone lipidic core nanocapsules with anisamide-polymethacrylate shell for colon cancer cells overexpressing sigma receptors.

The biggest challenge in colorectal cancer therapy is to avoid intestinal drug absorption before reaching the colon, while focusing on tumor specific delivery with high local concentration and minimal toxicity. In our work, thymoquinone (TQ)-loaded polymeric nanocapsules were prepared using the nanoprecipitation technique using Eudragit S100 as polymeric shell. Conjugation of anisamide as a targeting ligand for sigma receptors overexpressed by colon cancer cells to Eudragit S100 was carried out via carbodiimide coupling reaction, and was confirmed by thin layer chromatography and 1H-NMR. TQ nanocapsules were characterized for particle size, surface morphology, zeta potential, entrapment efficiency % (EE%), in vitro drug release and physical stability. A cytotoxicity study on three colon cancer cell lines (HT-29, HCT-116, Caco-2) was performed. Results revealed that the polymeric nanocapsules were successfully prepared, and the in vitro characterization showed a suitable size, zeta potential, EE% and physical stability. TQ exhibited a delayed release pattern from the nanocapsules in vitro. Anisamide-targeted TQ nanocapsules showed higher cytotoxicity against HT-29 cells overexpressing sigma receptors compared to their non-targeted counterparts and free TQ after incubation for 48 h, hence delineating anisamide as a promising ligand for active colon cancer targeting.

Prediction of Drug Loading in the Gelatin Matrix Using Computational Methods.

The delivery of drugs is a topic of intense research activity in both academia and industry with potential for positive economic, health, and societal impacts. The selection of the appropriate formulation (carrier and drug) with optimal delivery is a challenge investigated by researchers in academia and industry, in which millions of dollars are invested annually. Experiments involving different carriers and determination of their capacity for drug loading are very time-consuming and therefore expensive; consequently, approaches that employ computational/theoretical chemistry to speed have the potential to make hugely beneficial economic, environmental, and health impacts through savings in costs associated with chemicals (and their safe disposal) and time. Here, we report the use of computational tools (data mining of the available literature, principal component analysis, hierarchical clustering analysis, partial least squares regression, autocovariance calculations, molecular dynamics simulations, and molecular docking) to successfully predict drug loading into model drug delivery systems (gelatin nanospheres). We believe that this methodology has the potential to lead to significant change in drug formulation studies across the world.

Gelatin Nanoparticles.

Currently, gelatin nanoparticles are gaining more grounds in drug and gene delivery throughout all the available several routes of administration. Yet, the homogenous and less disperse preparation of this type of nanoparticles is still a challenging task due to the variation of the gelatin quality according to its source and due to its variable molecular weight. Accordingly, several methods were proposed from which the double-desolvation method has been proven to yield optimum results regarding particle size and homogeneity. Thereby, we describe in this chapter a detailed procedure of this method. We also introduce our protocols of the cationization of this kind of nanoparticles as it is extensively needed in case of loading genetic materials or proteins. Additionally, FITC labeling of gelatin nanoparticles that is usually utilized for purposes of imaging or bio-distribution studies is also introduced step by step.

Synthesis of CdS-modified chitosan quantum dots for the drug delivery of Sesamol.

Natural products continued to be the treasure trove for the discovery of anticancer drugs. The activity of certain natural products significantly increased with the use of novel drug delivery systems (DDS). Aiming to improve the activity of a natural cytotoxic agent; Sesamol, it was loaded onto cadmium sulfide (CdS) quantum dots (QDs) modified chitosan (CTS). The formed complex Sesamol-CdS@CTS was characterized using X-ray diffraction, UV-Vis absorption spectroscopy, thermogravimetric analysis, dynamic light scattering, Zeta-potential, Fourier transform infrared spectroscopy, fluorescence emission, high resolution transmission electron microscopy, and selected area electron diffraction. The concentration effect of Cd2+ and chitosan on the particle size of CdS QDs was investigated. The cytotoxic activity assay showed that Sesamol-CdS@CTS was more effective against cancer cells compared to the drug alone. The results showed that the half maximal inhibitory concentration values (IC50) of CdS@CTS, Sesamol, and Sesamol-CdS@CTS were 1730 ± 54, 495 ± 16.4, and 117 ± 3.2 µg/mL, respectively. Our results indicated that CdS@CTS exhibited high loading efficiency, and can be used for drug delivery.

Nanodiamonds: Minuscule gems that ferry antineoplastic drugs to resistant tumors.

Remarkable efforts are currently devoted to the area of nanodiamonds (NDs) research due to their superior properties viz: biocompatibility, minute size, inert core, and tunable surface chemistry. The use of NDs for the delivery of anticancer drugs has been at the forefront of NDs applications owing to their ability to increase chemosensitivity, sustain drug release, and minimize drug side effects. Accelerated steps towards the move of NDs from bench side to bedside have been recently witnessed. In this review, the effects of NDs production and purification techniques on NDs' final properties are discussed. Special concern is given to studies focusing on NDs use for anticancer drug delivery, stability enhancement and mediated targeted delivery. The aim of this review is to put the results of studies oriented towards NDs-mediated anticancer drug delivery side by side such that the reader can assess the potential use of NDs in clinics and follow up the upcoming results of clinical testing of NDs on animals and humans.

Gelatinized core liposomes: A new Trojan horse for the development of a novel timolol maleate glaucoma medication.

Glaucoma treatment with ocular medications requires overcoming the corneal barrier to drug penetration. Liposomes have a great corneal penetration ability and affinity while suffering from poor stability and low entrapment of hydrophilic drugs accompanied by rapid drug release. This work aims to develop a new, effective and stable glaucoma medication with sustained drug release properties; Timolol maleate gelatinized core liposomes. A full factorial design was utilized to study the effects of three formulation variables on drug loading and vesicle particle size. Vesicles were prepared by the thin-film hydration method, and characterized for in-vitro drug release and stability. Intra-ocular pressure (IOP) reduction was evaluated in-vivo on glaucomatous rabbit's eyes. The safety profile was assessed using histopathological examinations. Gelatin significantly increased the drug entrapment percentage reaching 50% with a particle size of 38.81 µm. Sustained drug release was recorded compared to a marketed product and to a conventional liposomal formulation. The prepared vesicles caused the highest reduction in IOP accompanied by safe histological findings. This work provided a new, safe and effective ocular glaucoma medication; Timolol maleate gelatinized core liposomes, solving the main problems of ocular liposomal formulations of hydrophilic drugs, suitable for the pharmaceutical industry and comprising abundant and relatively cheap components.

Curcumin or bisdemethoxycurcumin for nose-to-brain treatment of Alzheimer disease? A bio/chemo-informatics case study.

The current study introduces a new idea of utilising several bio/chemoinformatics tools in comparing two bio-similar natural molecules viz. curcumin and bisdemethoxycurcumin (BDMC) in order to select a potential nose-to-brain remedy for Alzheimer disease. The comparison comprised several bio/chemo informatics tools. It encompassed all levels starting from loading the drug in a certain carrier; PLGA nanoparticles, to the biopharmaceutical level investigating the interaction with mucin and inhibition of P-gp blood-brain barrier efflux pumps. Finally, the therapeutic level was investigated by studying the interaction with pharmacological targets such as amyloid peptide plaques and cyclooxygenase2 enzyme responsible for the inflammatory reactions of the studied disease. The comparison revealed the superiority of curcumin over BDMC. Five new analogues were also hypothesised where diethoxybisdemethoxycurcumin was  recommended as a superior molecule. This work introduced the virtual utilisation of bio/chemo informatics tools as a reliable and economic alternative to the exhausting and resources-consuming wet-lab experimentation.

Gelatinized-core liposomes: Toward a more robust carrier for hydrophilic molecules.

The use of liposomes as a delivery system for hydrophobic and hydrophilic drugs is well recognized. However, they possess several limitations that remained unresolved, including stability problems, low entrapment of the hydrophilic drugs, and the subsequent rapid release. This study introduces a novel approach to incorporate gelatin in the liposomal core to overcome these limitations. A rheological study was conducted to select suitable masses of the gelatin used in the liposomal formulations. Moreover, a full-factorial experimental design was utilized to compare the newly produced gel-core liposomes to the conventional liposomes with respect to the amount of a model hydrophilic molecule loading. An advanced machine learning method, namely, artificial neural networks was utilized to capture the effects of gelatin and cholesterol incorporation in the liposomes on the entrapment efficiency. The results revealed the successful preparation of the novel vesicles and their superiority over the conventional liposomes in drug loading, sustaining the drug release and stability which pose the newly introduced liposomal system as a successful delivery carrier for hydrophilic molecules and drugs. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 3086-3092, 2017.

Self-assembled amphiphilic core-shell nanocarriers in line with the modern strategies for brain delivery.

Disorders of the central nervous system (CNS) represent increasing social and economic problems all over the world which makes the effective transport of drugs to the brain a crucial need. In the last decade, many strategies were introduced to deliver drugs to the brain trying to overcome the challenge of the blood brain barrier (BBB) using both invasive and non-invasive methods. Non-invasive strategy represented in the application of nanocarriers became very common. One of the most hopeful nanoscopic carriers for brain delivery is core-shell nanocarriers or polymeric micelles (PMs). They are more advantageous than other nanocarriers. They offer small size, ease of preparation, ease of sterilization and the possibility of surface modification with various ligands. Hence, the aim of this review is to discuss modern strategies for brain delivery, micelles as a successful delivery system for the brain and how micelles could be modified to act as "magic bullets" for brain delivery.

Cancer nanotheranostics: A review of the role of conjugated ligands for overexpressed receptors.

Cancer treatment using chemotherapy has many drawbacks because of its non-specificity, in which the chemotherapeutic agent attacks both normal and cancerous cells, leading to severe damage to the normal cells, especially rapidly proliferating ones. Cancer targeting enables the drug to kill only tumor cells without adversely affecting healthy tissues, which leads to the improvement of the patient's well-being. Nanoparticles offer several advantages in drug delivery such as enhancing the solubility of hydrophobic drugs, sustaining their release and prolonging their circulation time. The ability of nanoparticles to specifically target tumor cells makes them a useful delivery system for anticancer agents. The type of the delivery system and formulation additives used can also improve the delivery of the anticancer agent. This review highlights some of the most highly sought receptors to be targeted in selective cancer treatment. It also reports some of the recent advances in cancer targeting using drug-loaded ligand-conjugated nanocarriers.

Selecting optimum protein nano-carriers for natural polyphenols using chemoinformatics tools.

BACKGROUND: The normal fate of any natural product with a therapeutic potential is to be formulated into an effective medicine. However, the conventional methods of selecting the suitable formulations or carriers based on the formulator experiences, trials and errors as well as materials availability do not usually yield the optimal results. HYPOTHESIS: We hypothesize the possibility of the virtual optimum selection of a protein carrier for two polyphenolic compounds widely investigated for their chemopreventive effects; resveratrol and curcumin using a combination of some chemoinformatics tools. METHODS: Two protein-based nanoparticles namely; albumin and gelatin nanoparticles were compared as carriers for the two selected phytochemicals; resveratrol and curcumin. Resveratrol-albumin, resveratrol-gelatin and curcumin-albumin results were gathered from the literature. While, a new combination (formulation), comprising curcumin as the cargo and gelatin nanoparticles as the carrier, was prepared and evaluated as a potential medicine for breast cancer. Combined chemoinformatics tools, namely; molecular dynamics and molecular docking were used to determine the optimum carrier for each of the two chemopreventive agents. RESULTS: A new curcumin-gelatin nanoparticulate formulation was prepared and proven cytotoxic after an application period of 48h on MCF-7 breast cancer cell-lines scoring an IC50 value of 64.8µg/ml. The utilized chemoinformatics tools comprising the molecular dynamics simulations of the protein nano-particulate drug-carriers followed by the molecular docking of phytochemical drugs on these carriers could capture the optimum protein carrier for each of the tested phytochemical and hence propose a successful formulation. CONCLUSION: This study presents one in a series that proves the novel addressed concept of the utilization of computational tools rather than wet-lab experimentation in providing better selection of drug-carrier pairs aiming for better formulations and the subsequent successful therapeutic effects.

Towards better modelling of drug-loading in solid lipid nanoparticles: Molecular dynamics, docking experiments and Gaussian Processes machine learning.

This study represents one of the series applying computer-oriented processes and tools in digging for information, analysing data and finally extracting correlations and meaningful outcomes. In this context, binding energies could be used to model and predict the mass of loaded drugs in solid lipid nanoparticles after molecular docking of literature-gathered drugs using MOE® software package on molecularly simulated tripalmitin matrices using GROMACS®. Consequently, Gaussian processes as a supervised machine learning artificial intelligence technique were used to correlate the drugs' descriptors (e.g. M.W., xLogP, TPSA and fragment complexity) with their molecular docking binding energies. Lower percentage bias was obtained compared to previous studies which allows the accurate estimation of the loaded mass of any drug in the investigated solid lipid nanoparticles by just projecting its chemical structure to its main features (descriptors).