Computer-aided design of liposomal drugs: In silico prediction and experimental validation of drug candidates for liposomal remote loading

Nano-mupirocin as tumor-targeted antibiotic: Physicochemical, immunotoxicological and pharmacokinetic characterization, and effect on gut microbiome

Nano-mupirocin is a PEGylated nano-liposomal formulation of the antibiotic mupirocin, undergoing evaluation for treating infectious diseases and intratumor bacteria. Intratumoral microbiota play an important role in the regulation of tumor progression and therapeutic efficacy. However, antibiotic use to target intratumoral bacteria should be performed in a way that will not affect the gut microbiota, found to enable the efficacy of cancer treatments. Nano-mupirocin may offer such a selective treatment. Herein, we demonstrate the ability of Nano-mupirocin to successfully target tumor-residing Fusobacterium nucleatum without an immediate effect on the gut microbiome. In-depth characterization of this novel formulation was performed, and the main findings include: (i). the pharmacokinetic analysis of mupirocin administered as Nano-mupirocin vs mupirocin lithium (free drug) demonstrated that most of the Nano-mupirocin in plasma is liposome associated; (ii). microbiome analysis of rat feces showed no significant short-term difference between Nano-mupirocin, mupirocin lithium and controls; (iii). Nano-mupirocin was active against intratumoral F. nucleatum, a tumor promoting bacteria that accumulates in tumors of the AT3 mice model of breast cancer. These data suggest the ability of Nano-mupirocin to target tumor residing and promoting bacteria.

Advancing liposome technology for innovative strategies against malaria

This review discusses the potential of liposomes as drug delivery systems for antimalarial therapies. Malaria continues to be a significant cause of mortality and morbidity, particularly among children and pregnant women. Drug resistance due to patient non-compliance and troublesome side effects remains a significant challenge in antimalarial treatment. Liposomes, as targeted and efficient drug carriers, have garnered attention owing to their ability to address these issues. Liposomes encapsulate hydrophilic and/or hydrophobic drugs, thus providing comprehensive and suitable therapeutic drug delivery. Moreover, the potential of passive and active drug delivery enables drug concentration in specific target tissues while reducing adverse effects. However, successful liposome formulation is influenced by various factors, including drug physicochemical characteristics and physiological barriers encountered during drug delivery. To overcome these challenges, researchers have explored modifications in liposome nanocarriers to achieve efficient drug loading, controlled release, and system stability. Computational approaches have also been adopted to predict liposome system stability, membrane integrity, and drug-liposome interactions, improving formulation development efficiency. By leveraging computational methods, optimizing liposomal drug delivery systems holds promise for enhancing treatment efficacy and minimizing side effects in malaria therapy. This review consolidates the current understanding and highlights the potential of liposome strategies against malaria.

Advancements in nanoscale delivery systems: optimizing intermolecular interactions for superior drug encapsulation and precision release

Nanoscale preparations, such as nanoparticles, micelles, and liposomes, are increasingly recognized in pharmaceutical technology for their high capability in tailoring the pharmacokinetics of the encapsulated drug within the body. These preparations have great potential in extending drug half-life, reducing dosing frequency, mitigating drug side effects, and enhancing drug efficacy. Consequently, nanoscale preparations offer promising prospects for the treatment of metabolic disorders, malignant tumors, and various chronic diseases. Nevertheless, the complete clinical potential of nanoscale preparations remains untapped due to the challenges associated with low drug loading degrees and insufficient control over drug release. In this review, we comprehensively summarize the vital role of intermolecular interactions in enhancing encapsulation and controlling drug release within nanoscale delivery systems. Our analysis critically evaluates the characteristics of common intermolecular interactions and elucidates the techniques employed to assess them. Moreover, we highlight the significant potential of intermolecular interactions in clinical translation, particularly in the screening and optimization of preparation prescriptions. By attaining a deeper understanding of intermolecular interaction properties and mechanisms, we can adopt a more rational approach to designing drug carriers, leading to substantial advancements in the application and clinical transformation of nanoscale preparations. Moving forward, continued research in this field offers exciting prospects for unlocking the full clinical potential of nanoscale preparations and revolutionizing the field of drug delivery.

Deep learning for automatic organ and tumor segmentation in nanomedicine pharmacokinetics

Rationale: Multimodal imaging provides important pharmacokinetic and dosimetry information during nanomedicine development and optimization. However, accurate quantitation is time-consuming, resource intensive, and requires anatomical expertise. Methods: We present NanoMASK: a 3D U-Net adapted deep learning tool capable of rapid, automatic organ segmentation of multimodal imaging data that can output key clinical dosimetry metrics without manual intervention. This model was trained on 355 manually-contoured PET/CT data volumes of mice injected with a variety of nanomaterials and imaged over 48 hours. Results: NanoMASK produced 3-dimensional contours of the heart, lungs, liver, spleen, kidneys, and tumor with high volumetric accuracy (pan-organ average %DSC of 92.5). Pharmacokinetic metrics including %ID/cc, %ID, and SUVmax achieved correlation coefficients exceeding R = 0.987 and relative mean errors below 0.2%. NanoMASK was applied to novel datasets of lipid nanoparticles and antibody-drug conjugates with a minimal drop in accuracy, illustrating its generalizability to different classes of nanomedicines. Furthermore, 20 additional auto-segmentation models were developed using training data subsets based on image modality, experimental imaging timepoint, and tumor status. These were used to explore the fundamental biases and dependencies of auto-segmentation models built on a 3D U-Net architecture, revealing significant differential impacts on organ segmentation accuracy. Conclusions: NanoMASK is an easy-to-use, adaptable tool for improving accuracy and throughput in imaging-based pharmacokinetic studies of nanomedicine. It has been made publicly available to all readers for automatic segmentation and pharmacokinetic analysis across a diverse array of nanoparticles, expediting agent development.

Activity Models of Key GPCR Families in the Central Nervous System: A Tool for Many Purposes

G protein-coupled receptors (GPCRs) are targets of many drugs, of which ∼25% are indicated for central nervous system (CNS) disorders. Drug promiscuity affects their efficacy and safety profiles. Predicting the polypharmacology profile of compounds against GPCRs can thus provide a basis for producing more precise therapeutics by considering the targets and the anti-targets in that family of closely related proteins. We provide a tool for predicting the polypharmacology of compounds within prominent GPCR families in the CNS: serotonin, dopamine, histamine, muscarinic, opioid, and cannabinoid receptors. Our in-house algorithm, "iterative stochastic elimination" (ISE), produces high-quality ligand-based models for agonism and antagonism at 31 GPCRs. The ISE models correctly predict 68% of CNS drug-GPCR interactions, while the "similarity ensemble approach" predicts only 33%. The activity models correctly predict 56% of reported activities of DrugBank molecules for these CNS receptors. We conclude that the combination of interactions and activity profiles generated by screening through our models form the basis for subsequent designing and discovering novel therapeutics, either single, multitargeting, or repurposed.

Predicting the Likelihood of Molecules to Act as Modulators of Protein-Protein Interactions

Targeting protein-protein interactions (PPIs) by small molecule modulators (iPPIs) is an attractive strategy for drug therapy, and some iPPIs have already been introduced into the clinic. Blocking PPIs is however considered to be a more difficult task than inhibiting enzymes or antagonizing receptor activity. In this paper, we examine whether it is possible to predict the likelihood of molecules to act as iPPIs. Using our in-house iterative stochastic elimination (ISE) algorithm, we constructed two classification models that successfully distinguish between iPPIs from the iPPI-DB database and decoy molecules from either the Enamine HTS collection (ISE 1) or the ZINC database (ISE 2). External test sets of iPPIs taken from the TIMBAL database and decoys from Enamine HTS or ZINC were screened by the models: the area under the curve for the receiver operating characteristic curve was 0.85-0.89, and the Enrichment Factor increased from an initial 1 to as much as 66 for ISE 1 and 57 for ISE 2. Screening of the Enamine HTS and ZINC data sets through both models results in a library of ∼1.3 million molecules that pass either one of the models. This library is enriched with iPPI candidates that are structurally different from known iPPIs, and thus, it is useful for target-specific screenings and should accelerate the discovery of iPPI drug candidates. The entire library is available in Table S6.

In silico modeling and empirical study of 4-n-Butylresorcinol nanoliposome formulation

A study to incorporate in silico modeling with an empirical experiment has been carried out to formulate nanoliposome containing 4-n-butylresorcinol as the active ingredient. The in silico modeling was performed using molecular dynamics simulation followed by radius of gyration observation to provide insight into the mechanisms of 4-n-butylresorcinol stabilization by liposome due to their nano-size. The empirical experiment was conducted by formulating the nanoliposome using soy lecithin phospholipid formula as suggested by the in silico modeling followed by determining its particle size as well as its shape. From their incorporation, it was found that 3200 phospholipid molecules were selected in formulating nanoliposome containing 4-n-butylresorcinol. The results of the nanoliposomes size observation in the modeling of 3200 lipid molecules was 87.01 (± 0.59) nm, whereas the size from the empirical study was 87.57 (± 0.06) nm. Communicated by Ramaswamy H. Sarma.

Multifunctional Lipid Bilayer Nanocarriers for Cancer Immunotherapy in Heterogeneous Tumor Microenvironments, Combining Immunogenic Cell Death Stimuli with Immune Modulatory Drugs

In addition to the contribution of cancer cells, the solid tumor microenvironment (TME) has a critical role in determining tumor expansion, antitumor immunity, and the response to immunotherapy. Understanding the details of the complex interplay between cancer cells and components of the TME provides an unprecedented opportunity to explore combination therapy for intervening in the immune landscape to improve immunotherapy outcome. One approach is the introduction of multifunctional nanocarriers, capable of delivering drug combinations that provide immunogenic stimuli for improvement of tumor antigen presentation, contemporaneous with the delivery of coformulated drug or synthetic molecules that provide immune danger signals or interfere in immune-escape, immune-suppressive, and T-cell exclusion pathways. This forward-looking review will discuss the use of lipid-bilayer-encapsulated liposomes and mesoporous silica nanoparticles for combination immunotherapy of the heterogeneous immune landscapes in pancreatic ductal adenocarcinoma and triple-negative breast cancer. We describe how the combination of remote drug loading and lipid bilayer encapsulation is used for the synthesis of synergistic drug combinations that induce immunogenic cell death, interfere in the PD-1/PD-L1 axis, inhibit the indoleamine-pyrrole 2,3-dioxygenase (IDO-1) immune metabolic pathway, restore spatial access to activated T-cells to the cancer site, or reduce the impact of immunosuppressive stromal components. We show how an integration of current knowledge and future discovery can be used for a rational approach to nanoenabled cancer immunotherapy.

UV-DIB: label-free permeability determination using droplet interface bilayers

Simple diffusion of molecular entities through a phospholipid bilayer, is a phenomenon of great importance to the pharmaceutical and agricultural industries. Current model lipid systems to probe this typically only employ fluorescence as a readout, thus limiting the range of assessable chemical matter that can be studied. We report a new technology platform, the UV-DIB, which facilitates label free measurement of small molecule translocation rates. This is based upon the coupling of droplet interface bilayer technology with implemented fiber optics to facilitate analysis via ultraviolet spectroscopy, in custom designed PMMA wells. To improve on current DIB technology, the platform was designed to be reusable, with a high sampling rate and a limit of UV detection in the low μM regime. We demonstrate the use of our system to quantify passive diffusion in a reproducible and rapid manner where the system was validated by investigating multiple permeants of varying physicochemical properties across a range of lipid interfaces, each demonstrating differing kinetics. Our system permits the interrogation of structural dependence on the permeation rate of a given compound. We present this ability from two structural perspectives, that of the membrane, and the permeant. We observed a reduction in permeability between pure DOPC and DPhPC interfaces, concurring with literature and demonstrating our ability to study the effects of lipid composition on permeability. In relation to the effects of permeant structure, our device facilitated the rank ordering of various compounds from the xanthine class of compounds, where the structure of each permeant differed by a single group alteration. We found that DIBs were stable up to 5% DMSO, a molecule often used to aid solubilisation of pharmaceutical and agrochemical compounds. The ability of our device to rank-order compounds with such minor structural differences provides a level of precision that is rarely seen in current, industrially applied technologies.

Therapeutic Potential of Injectable Nano-Mupirocin Liposomes for Infections Involving Multidrug-Resistant Bacteria

Antibiotic resistance is a global health threat. There are a few antibiotics under development, and even fewer with new modes of action and no cross-resistance to established antibiotics. Accordingly, reformulation of old antibiotics to overcome resistance is attractive. Nano-mupirocin is a PEGylated nano-liposomal formulation of mupirocin, potentially enabling parenteral use in deep infections, as previously demonstrated in several animal models. Here, we describe extensive in vitro profiling of mupirocin and Nano-mupirocin and correlate the resulting MIC data with the pharmacokinetic profiles seen for Nano-mupirocin in a rat model. Nano-mupirocin showed no cross-resistance with other antibiotics and retained full activity against vancomycin-, daptomycin-, linezolid- and methicillin- resistant Staphylococcus aureus, against vancomycin-resistant Enterococcus faecium, and cephalosporin-resistant Neisseria gonorrhoeae. Following Nano-mupirocin injection to rats, plasma levels greatly exceeded relevant MICs for >24 h, and a biodistribution study in mice showed that mupirocin concentrations in vaginal secretions greatly exceeded the MIC₉₀ for N. gonorrhoeae (0.03 µg/mL) for >24 h. In summary, Nano-mupirocin has excellent potential for treatment of several infection types involving multiresistant bacteria. It has the concomitant benefits from utilizing an established antibiotic and liposomes of the same size and lipid composition as Doxil^®, an anticancer drug product now used for the treatment of over 700,000 patients globally.

Doxorubicin Hydrochloride-Loaded Nonionic Surfactant Vesicles to Treat Metastatic and Non-Metastatic Breast Cancer

Doxorubicin hydrochloride (DOX) is currently used to treat orthotropic and metastatic breast cancer. Because of its side effects, the use of DOX in cancer patients is sometimes limited; for this reason, several scientists tried designing drug delivery systems which can improve drug therapeutic efficacy and decrease its side effects. In this study, we designed, prepared, and physiochemically characterized nonionic surfactant vesicles (NSVs) which are obtained by self-assembling different combinations of hydrophilic (Tween 20) and hydrophobic (Span 20) surfactants, with cholesterol. DOX was loaded in NSVs using a passive and pH gradient remote loading procedure, which increased drug loading from ∼1 to ∼45%. NSVs were analyzed in terms of size, shape, size distribution, zeta potential, long-term stability, entrapment efficiency, and release kinetics, and nanocarriers having the best physiochemical parameters were selected for further in vitro tests. NSVs with and without DOX were stable and showed a sustained drug release up to 72 h. In vitro studies, with MCF-7 and MDA MB 468 cells, demonstrated that NSVs, containing Span 20, were better internalized in MCF-7 and MDA MB 468 cells than NSVs with Tween 20. NSVs increased the anticancer effect of DOX in MCF-7 and MDA MB 468 cells, and this effect is time and dose dependent. In vitro studies using metastatic and nonmetastatic breast cancer cells also demonstrated that NSVs, containing Span 20, had higher cytotoxicity than NSVs with Tween 20. The resulting data suggested that DOX-loaded NSVs could be a promising nanocarrier for the potential treatment of metastatic breast cancer.

Effect of the ammonium salt anion on the structure of doxorubicin complex and PEGylated liposomal doxorubicin nanodrugs

BACKGROUND

In Doxil®, PEGylated nanoliposomes are created by hydration of the lipids in ammonium sulfate, and are remotely loaded with doxorubicin by a transmembrane ammonium gradient. The ammonium sulfate is then removed from the external aqueous phase, surrounding the liposomes, and replaced by an isoosmotic sucrose solution in 10 mM histidine buffer at pH 6.5.

METHODS

We prepared PEGylated liposomal doxorubicin (PLD) with a series of ammonium monovalent salts that after remote loading became the intraliposome doxorubicin counteranions. We analyzed the liposomes by solution X-ray scattering, differential scanning calorimetry, and electron micropscopy.

RESULTS

PLDs prepared with sulfonic acid derivatives as counteranion exhibited chemical and physical stabilities. We determined the effect of these ammonium salt counteranions on the structure, morphology, and thermotropic behavior of the PEGylated nanoliposomes, formed before and after doxorubicin loading, and the bulk properties of the doxorubicin-counteranion complexes. By comparing the structure of the doxorubicin complexes in the bulk and inside the nanoliposomes, we revealed the effect of confinement on the structure and doxorubicin release rate for each of the derivatives of the ammonium sulfonic acid counteranions.

CONCLUSIONS

We found that the extent and direction of the doxorubicin confinement effect and its release rate were strongly dependent on the type of counteranion. The counteranions, however, neither affected the structure and thermotropic behavior of the liposome membrane, nor the thickness and density of the liposome PEG layers. In an additional study, it was demonstrated that PLD made with ammonium-methane sulfonate exhibit a much lower Hand and Foot syndrome.

GENERAL SIGNIFICANCE

The structure, physical state, and pharmacokinetics of doxorubicin in PEGylated nanoliposomes, prepared by transmembrane remote loading using gradients of ammonium salts, strongly depend on the counteranions.

Immune checkpoint inhibition in syngeneic mouse cancer models by a silicasome nanocarrier delivering a GSK3 inhibitor

Checkpoint blocking antibodies that interfere in the PD-1/PD-L1 axis provide effective cancer immunotherapy for tumors that are immune inflamed or induced to become "hot". It has also been demonstrated that a small molecule inhibitor of the signaling hub kinase GSK3 can interfere in the PD-1/PD-L1 axis in T-cells by suppressing PD-1 expression. This provides an alternative approach to intervening in the PD-1/PD-L1 axis to provide cancer immunotherapy. In this communication, we demonstrate the remote loading of GSK3 inhibitor AZD1080 into the porous interior of mesoporous silica nanoparticles coated with a lipid bilayer (a.k.a. silicasomes). In a MC38 colon cancer model, intravenous injection (IV) of silicasome-encapsulated AZD1080 significantly improved biodistribution and drug delivery to the tumor site. The improved drug delivery was accompanied by cytotoxic MC38 tumor cell killing by perforin-releasing CD8⁺ T-cells, exhibiting reduced PD-1 expression. IV injection of encapsulated AZD1080 also resulted in significant tumor shrinkage in other syngeneic mouse tumor models, including another colorectal tumor (CT26), as well as pancreas (KPC) and lung (LLC) cancer models. Not only was the therapeutic efficacy of encapsulated AZD1080 similar or better than anti-PD-1 antibody, but the treatment was devoid of treatment toxicity. These results provide proof-of-principal demonstration of the feasibility of using encapsulated delivery of a GSK3 inhibitor to provide cancer immunotherapy, with the possibility to be used as a monotherapy or in combination with chemotherapy or other immunomodulatory agents.

Mechanistic Understanding From Molecular Dynamics Simulation in Pharmaceutical Research 1: Drug Delivery

In this review, we outline the growing role that molecular dynamics simulation is able to play as a design tool in drug delivery. We cover both the pharmaceutical and computational backgrounds, in a pedagogical fashion, as this review is designed to be equally accessible to pharmaceutical researchers interested in what this new computational tool is capable of and experts in molecular modeling who wish to pursue pharmaceutical applications as a context for their research. The field has become too broad for us to concisely describe all work that has been carried out; many comprehensive reviews on subtopics of this area are cited. We discuss the insight molecular dynamics modeling has provided in dissolution and solubility, however, the majority of the discussion is focused on nanomedicine: the development of nanoscale drug delivery vehicles. Here we focus on three areas where molecular dynamics modeling has had a particularly strong impact: (1) behavior in the bloodstream and protective polymer corona, (2) Drug loading and controlled release, and (3) Nanoparticle interaction with both model and biological membranes. We conclude with some thoughts on the role that molecular dynamics simulation can grow to play in the development of new drug delivery systems.

DNA-Controlled Encapsulation of Small Molecules in Protein Nanoparticles

A nanoparticle can hold multiple types of therapeutic and imaging agents for disease treatment and diagnosis. However, controlling the storage of molecules in nanoparticles is challenging, because nonspecific intermolecular interactions are used for encapsulation. Here, we used specific DNA interactions to store molecules in nanoparticles. We made nanoparticles containing DNA anchors to capture DNA-conjugated small molecules. By changing the sequences and stoichiometry of DNA anchors, we can control the amount and ratio of molecules with different chemical properties in the nanoparticles. We modified the cytotoxicity of our nanoparticles to cancer cells by changing the ratio of encapsulated drugs (mertansine and doxorubicin). Specifically controlling the storage of multiple types of molecules allows us to optimize the properties of combination drug and imaging nanoparticles.

Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine-tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient-specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.

Novel poly(2-oxazoline) block copolymer with aromatic heterocyclic side chains as a drug delivery platform

Here we report a novel poly(2-oxazoline)-based block copolymer with the aromatic heterocyclic side chains in one block, poly(2-methyl-2-oxazoline)-b-poly(2-N,N-dimethyl-1,3,5-triazine-2,4-diamine-6-ethyl-2-oxazoline) (PMeOx-PcBOx), and demonstrate its potential application as a drug delivery platform. The copolymer was synthesized via the condensation of N,N-dimethylbiguanide with the methyl ester side chain in poly(2-methoxycarboxyethyl-2-oxazoline) block (PMestOx) of the PMeOx-PMestOx diblock copolymer. We confirmed the N,N-dimethylbiguanide condensation with PMestOx and the complete conversion of the side chain to the N,N-dimethyl-1,3,5-triazine-2,4-diamine-6-ethyl moiety by NMR spectroscopy, MALDI-TOF mass spectroscopy, UV-Vis spectroscopy, and titration analysis. The PMeOx-PcBOx copolymer self-assemble into polymeric micelles in aqueous solution. Successful encapsulation into these micelles has been demonstrated for 1) several poorly soluble drugs, such as bruceantin and LY2109761, and 2) dichloro(1,2-diaminocyclohexane)platinum(II) (DachPt). The first class of drugs is incorporated possibly via hydrogen bonding and pi-pi interactions with the PcBOx side groups, while the second one is likely forms coordination bonds with the same side groups. The capability of this new copolymer to solubilize a uniquely diverse set of active pharmaceutical ingredients suggests potential applications in drug delivery.

New drug candidates for liposomal delivery identified by computer modeling of liposomes' remote loading and leakage

Remote drug loading into nano-liposomes is in most cases the best method for achieving high concentrations of active pharmaceutical ingredients (API) per nano-liposome that enable therapeutically viable API-loaded nano-liposomes, referred to as nano-drugs. This approach also enables controlled drug release. Recently, we constructed computational models to identify APIs that can achieve the desired high concentrations in nano-liposomes by remote loading. While those previous models included a broad spectrum of experimental conditions and dealt only with loading, here we reduced the scope to the molecular characteristics alone. We model and predict API suitability for nano-liposomal delivery by fixing the main experimental conditions: liposome lipid composition and size to be similar to those of Doxil® liposomes. On that basis, we add a prediction of drug leakage from the nano-liposomes during storage. The latter is critical for having pharmaceutically viable nano-drugs. The "load and leak" models were used to screen two large molecular databases in search of candidate APIs for delivery by nano-liposomes. The distribution of positive instances in both loading and leakage models was similar in the two databases screened. The screening process identified 667 molecules that were positives by both loading and leakage models (i.e., both high-loading and stable). Among them, 318 molecules received a high score in both properties and of these, 67 are FDA-approved drugs. This group of molecules, having diverse pharmacological activities, may be the basis for future liposomal drug development.

Exposing the Molecular Screening Method of Indonesian Natural Products Derivate as Drug Candidates for Cervical Cancer

The menace of cervical cancer has reached an alarming rate. There are more than 450.000 cases of cervical cancer yearly, with mortality rate of about 50%. This deadly cancer is caused by human papillomavirus (HPV), mainly subtypes 16 and 18. The pharmaceutical industry has produced drug for combating the virus, known as SAHA (suberoylanilide hydroxamic acid). It inhibits class II HDAC Homo sapiens (HDACi). The utilization of SAHA has some side effects, one of which is bone loss. Thus, searching for viable alternatives aside SAHA is inevitable. The objective of this research is to investigate the molecular interaction of selected Indonesian natural products with class II HDAC Homo sapiens. LigX tool in MOE 2008.10 was used as an instrument to investigate the molecular interaction. Then, computer-aided drug discovery and development (CADDD) approach involving molecular docking and dynamics methods was utilized to screen the natural products library. In the end, we found that herbaric acid could act as a potential drug candidate for cervical cancer.

Molecular insights into the formation of drug-monoacyl phosphatidylcholine solid dispersions for oral delivery

Phospholipid-based formulations provide a key technology to formulate poorly water-soluble drugs. A recent interest has been in using phospholipids with a high content of monoacyl phosphatidylcholine (monoacyl PC) due to its ability to form mixed micelles of mono- and di-acylphospholipids upon aqueous dispersion. The present work focused on binary drug- monoacyl PC systems (at about equimolar ratio) with respect to screening of solid dispersion feasibility. It was tested whether or not a molecular rule of thumb can predict the desirable absence of drug crystallinity in the products. Subsequently, molecular simulations were performed to gain a better understanding of molecular association between drugs and monoacyl PC. Finally, the glass-forming ability (GFA) of pure drugs was considered with respect to solid dispersion formation. All products were obtained from a solvent-evaporation process and subsequent analysis of potential drug crystallinity was measured with X-ray powder diffraction and differential scanning calorimetry. Molecular simulations were making use of a Monte Carlo algorithm and molecular properties relevant for GFA were calculated. As a result, the dataset of 28 drugs confirmed an earlier proposed empirical rule that enthalpy of fusion and logP were important for solid dispersion formation, while some relevance was also evidenced for drug energies of frontal orbitals. Interestingly, the Monte Carlo simulations revealed several likely associations between drug and phospholipid rather than a well-defined single complex formation. However, drug-excipient interactions were still pivotal, since GFA of pure drug could not predict solid dispersion formation. These findings led to important molecular insights into binary solid dispersions of drug and monoacyl PC, which can guide formulators in early drug product development.

Dependence of norfloxacin diffusion across bilayers on lipid composition

Antibiotic resistance is a growing concern in medicine and raises the need to develop and design new drug molecules that can efficiently inhibit bacterial replication. Spurring the passive uptake of the drug molecules is an obvious solution. However our limited understanding of drug-membrane interactions due to the presence of an overwhelming variety of lipids constituting cellular membranes and the lack of facile tools to probe the bio-physical interactions between drugs and lipids imposes a major challenge towards developing new drug molecules that can enter the cell via passive diffusion. Here, we used a label-free micro-fluidic platform combined with giant unilamellar lipid vesicles to investigate the permeability of membranes containing mixtures of DOPE and DOPG in DOPC, leading to a label-free measurement of passive membrane-permeability of autofluorescent antibiotics. A fluoroquinolone drug, norfloxacin was used as a case study. Our results indicate that the diffusion of norfloxacin is strongly dependent on the lipid composition which is not expected from the traditional octanol-lipid partition co-efficient assay. The anionic lipid, DOPG, slows the diffusion process whereas the diffusion across liposomes containing DOPE increases with higher DOPE concentration. Our findings emphasise the need to investigate drug-membrane interactions with focus on the specificity of drugs to lipids for efficient drug delivery, drug encapsulation and targeted drug-delivery.

Nano-mupirocin: enabling the parenteral activity of mupirocin

Mupirocin is an antibiotic having a unique mode of action, not shared by any other therapeutically available antibiotic. However, due to its rapid elimination following injection and high protein binding, current therapeutic use is limited to topical administration. Computational methods have identified mupirocin as a good candidate for delivery via long-circulating nano-liposomes. Formulating mupirocin in such liposomes to form Nano-mupirocin protects the drug in the circulation, enabling therapeutic efficacy. This was demonstrated using two different animal models that served as a proof of concept: the mice

Comparison of two methods forecasting binding rate of plasma protein

By introducing the descriptors calculated from the molecular structure, the binding rates of plasma protein (BRPP) with seventy diverse drugs are modeled by a quantitative structure-activity relationship (QSAR) technique. Two algorithms, heuristic algorithm (HA) and support vector machine (SVM), are used to establish linear and nonlinear models to forecast BRPP. Empirical analysis shows that there are good performances for HA and SVM with cross-validation correlation coefficients R_cv² of 0.80 and 0.83. Comparing HA with SVM, it was found that SVM has more stability and more robustness to forecast BRPP.

Extracellular vesicles as drug delivery systems: lessons from the liposome field

Extracellular vesicles (EVs) are membrane-derived particles surrounded by a (phospho)lipid bilayer that are released by cells in the human body. In addition to direct cell-to-cell contact and the secretion of soluble factors, EVs function as another mechanism of intercellular communication. These vesicles are able to efficiently deliver their parental cell-derived molecular cargo to recipient cells, which can result in structural changes at an RNA, protein, or even phenotypic level. For this reason, EVs have recently gained much interest for drug delivery purposes. In contrast to these 'natural delivery systems', synthetic (phospho)lipid vesicles, or liposomes, have been employed as drug carriers for decades, resulting in several approved liposomal nanomedicines used in the clinic. This review discusses the similarities and differences between EVs and liposomes with the focus on features that are relevant for drug delivery purposes such as circulation time, biodistribution, cellular interactions and cargo loading. By applying beneficial features of EVs to liposomes and vice versa, improved drug carriers can be developed which will advance the field of nanomedicines and ultimately improve patient outcomes. While the application of EVs for therapeutic drug delivery is still in its infancy, issues regarding the understanding of EV biogenesis, large-scale production and in vivo interactions need to be addressed in order to develop successful and cost-effective EV-based drug delivery systems.