Recent Updates of the CRISPR/Cas9 Genome Editing System: Novel Approaches to Regulate Its Spatiotemporal Control by Genetic and Physicochemical Strategies.

The genome editing approach by clustered regularly interspaced short palindromic repeats (CRISPR)/associated protein 9 (CRISPR/Cas9) is a revolutionary advancement in genetic engineering. Owing to its simple design and powerful genome-editing capability, it offers a promising strategy for the treatment of different infectious, metabolic, and genetic diseases. The crystal structure of Streptococcus pyogenes Cas9 (SpCas9) in complex with sgRNA and its target DNA at 2.5 Å resolution reveals a groove accommodating sgRNA:DNA heteroduplex within a bilobate architecture with target recognition (REC) and nuclease (NUC) domains. The presence of a PAM is significantly required for target recognition, R-loop formation, and strand scission. Recently, the spatiotemporal control of CRISPR/Cas9 genome editing has been considerably improved by genetic, chemical, and physical regulatory strategies. The use of genetic modifiers anti-CRISPR proteins, cell-specific promoters, and histone acetyl transferases has uplifted the application of CRISPR/Cas9 as a future-generation genome editing tool. In addition, interventions by chemical control, small-molecule activators, oligonucleotide conjugates and bioresponsive delivery carriers have improved its application in other areas of biological fields. Furthermore, the intermediation of physical control by using heat-, light-, magnetism-, and ultrasound-responsive elements attached to this molecular tool has revolutionized genome editing further. These strategies significantly reduce CRISPR/Cas9's undesirable off-target effects. However, other undesirable effects still offer some challenges for comprehensive clinical translation using this genome-editing approach. In this review, we summarize recent advances in CRISPR/Cas9 structure, mechanistic action, and the role of small-molecule activators, inhibitors, promoters, and physical approaches. Finally, off-target measurement approaches, challenges, future prospects, and clinical applications are discussed.

Computational exploration and design of a multi-epitopes vaccine construct against Chlamydia psittaci.

Chlamydia psittaci is an intracellular pathogen and causes variety of deadly infections in humans. Antibiotics are effective against C. psittaci however high percentage of resistant strains have been reported in recent times. As there is no licensed vaccine, we used in-silico techniques to design a multi-epitopes vaccine against C. psittaci. Following a step-wise protocol, the proteome of available 26 strains was retrieved and filtered for subcellular localized proteins. Five proteins were selected (2 extracellular and 3 outer membrane) and were further analyzed for B-cell and T-cell epitopes prediction. Epitopes were further checked for antigenicity, solubility, stability, toxigenicity, allergenicity, and adhesive properties. Filtered epitopes were linked via linkers and the 3D structure of the designed vaccine construct was predicted. Binding of the designed vaccine with immune receptors: MHC-I, MHC-II, and TLR-4 was analyzed, which resulted in docking energy scores of -4.37 kcal/mol, -0.20 kcal/mol and -22.38 kcal/mol, respectively. Further, the docked complexes showed stable dynamics with a maximum value of vaccine-MHC-I complex (7.8 Å), vaccine-MHC-II complex (6.2 Å) and vaccine-TLR4 complex (5.2 Å). As per the results, the designed vaccine construct reported robust immune responses to protect the host against C. psittaci infections. In the study, the C. psittaci proteomes were considered in pan-genome analysis to extract core proteins. The pan-genome analysis was conducted using bacterial pan-genome analysis (BPGA) software. The core proteins were checked further for non-redundant proteins using a CD-Hit server. Surface localized proteins were investigated using PSORTb v 3.0. The surface proteins were BLASTp against Virulence Factor Data Base (VFDB) to predict virulent factors. Antigenicity prediction of the shortlisted proteins was further done using VAXIGEN v 2.0. The epitope mapping was done using the immune epitope database (IEDB). A multi-epitopes vaccine was built and a 3D structure was generated using 3Dprot online server. The docking analysis of the designed vaccine with immune receptors was carried out using PATCHDOCK. Molecular dynamics and post-simulation analyses were carried out using AMBER v20 to decipher the dynamics stability and intermolecular binding energies of the docked complexes.Communicated by Ramaswamy H. Sarma.

Nano-Emulsion Based Gel for Topical Delivery of an Anti-Inflammatory Drug: In vitro and in vivo Evaluation.

Introduction: Arthritic disorder is a common disease in elderly patients and the most common cause of joint dysfunction. This study aims to design Piroxicam-loaded nanoemulsion (PXM-NE) formulations to enhance the analgesic and anti-inflammatory activity of the drug for topical use. Methods: The nanoemulsion preparations were designed based on a high-pressure homogenization technique and were characterized for particle size (PS), poly dispersity index (Pi), zeta potential (ZP), drug content, and the selected formula was investigated for its topical analgesic activity and pharmacokinetic parameters. Results: The characterizations showed that the PS was 310.20±19.84 nm, Pi was 0.15±0.02, and ZP was -15.74±1.6 mV for the selected formula. A morphology study showed that the PXM-NE droplets were spherical with a uniform size distribution. The in vitro release study showed a biphasic release pattern with a rapid release within the first 2 hours followed by a sustained release pattern. The analgesic activity for optimal formula was 1.66 times higher than the commercial gel with a double duration of analgesic activity. The Cmax was 45.73±9.95 and 28.48±6.44 ng/mL for the gel form of the selected formula and the commercial gel respectively. The relevant bioavailability of the selected formula was 2.41 higher than the commercial gel. Conclusion: The results showed good physicochemical properties, higher bioavailability, and a longer analgesic effect of PXM from nanoemulsion gel, as compared to the commercial product.

Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models.

Particle size, shape and morphology can be considered as the most significant functional parameters, their effects on increasing the performance of oral solid dosage formulation are indisputable. Supercritical Carbon dioxide fluid (SCCO2) technology is an effective approach to control the above-mentioned parameters in oral solid dosage formulation. In this study, drug solubility measuring is investigated based on artificial intelligence model using carbon dioxide as a common supercritical solvent, at different pressure and temperature, 120-400 bar, 308-338 K. The results indicate that pressure has a strong effect on drug solubility. In this investigation, Decision Tree (DT), Adaptive Boosted Decision Trees (ADA-DT), and Nu-SVR regression models are used for the first time as a novel model on the available data, which have two inputs, including pressure, X1 = P(bar) and temperature, X2 = T(K). Also, output is Y = solubility. With an R-squared score, DT, ADA-DT, and Nu-SVR showed results of 0.836, 0.921, and 0.813. Also, in terms of MAE, they showed error rates of 4.30E-06, 1.95E-06, and 3.45E-06. Another metric is RMSE, in which DT, ADA-DT, and Nu-SVR showed error rates of 4.96E-06, 2.34E-06, and 5.26E-06, respectively. Due to the analysis outputs, ADA-DT selected as the best and novel model and the find optimal outputs can be shown via vector: (x1 = 309, x2 = 317.39, Y1 = 7.03e-05).

Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent.

Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO2 was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308-338 K, and pressure 120-400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R2-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E-04, 7.40E-05, and 9.73E-06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E-01, DTR shows an error rate equal to 1.63E-01, and GPR as the best mode illustrates 5.06E-02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E-03).

Application of CO2 Supercritical Fluid to Optimize the Solubility of Oxaprozin: Development of Novel Machine Learning Predictive Models.

Over the last years, extensive motivation has emerged towards the application of supercritical carbon dioxide (SCCO2) for particle engineering. SCCO2 has great potential for application as a green and eco-friendly technique to reach small crystalline particles with narrow particle size distribution. In this paper, an artificial intelligence (AI) method has been used as an efficient and versatile tool to predict and consequently optimize the solubility of oxaprozin in SCCO2 systems. Three learning methods, including multi-layer perceptron (MLP), Kriging or Gaussian process regression (GPR), and k-nearest neighbors (KNN) are selected to make models on the tiny dataset. The dataset includes 32 data points with two input parameters (temperature and pressure) and one output (solubility). The optimized models were tested with standard metrics. MLP, GPR, and KNN have error rates of 2.079 × 10-8, 2.173 × 10-9, and 1.372 × 10-8, respectively, using MSE metrics. Additionally, in terms of R-squared, they have scores of 0.868, 0.997, and 0.999, respectively. The optimal inputs are the same as the maximum possible values and are paired with a solubility of 1.26 × 10-3 as an output.

Development of GBRT Model as a Novel and Robust Mathematical Model to Predict and Optimize the Solubility of Decitabine as an Anti-Cancer Drug.

The efficient production of solid-dosage oral formulations using eco-friendly supercritical solvents is known as a breakthrough technology towards developing cost-effective therapeutic drugs. Drug solubility is a significant parameter which must be measured before designing the process. Decitabine belongs to the antimetabolite class of chemotherapy agents applied for the treatment of patients with myelodysplastic syndrome (MDS). In recent years, the prediction of drug solubility by applying mathematical models through artificial intelligence (AI) has become known as an interesting topic due to the high cost of experimental investigations. The purpose of this study is to develop various machine-learning-based models to estimate the optimum solubility of the anti-cancer drug decitabine, to evaluate the effects of pressure and temperature on it. To make models on a small dataset in this research, we used three ensemble methods, Random Forest (RFR), Extra Tree (ETR), and Gradient Boosted Regression Trees (GBRT). Different configurations were tested, and optimal hyper-parameters were found. Then, the final models were assessed using standard metrics. RFR, ETR, and GBRT had R2 scores of 0.925, 0.999, and 0.999, respectively. Furthermore, the MAPE metric error rates were 1.423 × 10-1 7.573 × 10-2, and 7.119 × 10-2, respectively. According to these facts, GBRT was considered as the primary model in this paper. Using this method, the optimal amounts are calculated as: P = 380.88 bar, T = 333.01 K, Y = 0.001073.

Design of predictive model to optimize the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug.

These days, many efforts have been made to increase and develop the solubility and bioavailability of novel therapeutic medicines. One of the most believable approaches is the operation of supercritical carbon dioxide fluid (SC-CO2). This operation has been used as a unique method in pharmacology due to the brilliant positive points such as colorless nature, cost-effectives, and environmentally friendly. This research project is aimed to mathematically calculate the solubility of Oxaprozin in SC-CO2 through artificial intelligence. Oxaprozin is a nonsteroidal anti-inflammatory drug which is useful in arthritis disease to improve swelling and pain. Oxaprozin is a type of BCS class II (Biopharmaceutical Classification) drug with low solubility and bioavailability. Here in order to optimize and improve the solubility of Oxaprozin, three ensemble decision tree-based models including random forest (RF), Extremely random trees (ET), and gradient boosting (GB) are considered. 32 data vectors are used for this modeling, moreover, temperature and pressure as inputs, and drug solubility as output. Using the MSE metric, ET, RF, and GB illustrated error rates of 6.29E-09, 9.71E-09, and 3.78E-11. Then, using the R-squared metric, they demonstrated results including 0.999, 0.984, and 0.999, respectively. GB is selected as the best fitted model with the optimal values including 33.15 (K) for the temperature, 380.4 (bar) for the pressure and 0.001242 (mole fraction) as optimized value for the solubility.

Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning.

Accurate specification of the drugs' solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).

Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization.

Industrial-based application of supercritical CO2 (SCCO2) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C15H18O3) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO2, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10-4, 7.814 × 10-5, and 1.664 × 10-4 for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10-4, 6.197 × 10-5, and 8.777 × 10-5errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x1 = 40.0, x2 = 338.0, Y = 1.27 × 10-3) vector.

Intranasal Delivery of Granisetron to the Brain via Nanostructured Cubosomes-Based In Situ Gel for Improved Management of Chemotherapy-Induced Emesis.

This research aimed to boost granisetron (GS) delivery to the brain via the intranasal route to better manage chemotherapy-induced emesis. Glycerol monooleate (GMO), Poloxamer 407 (P 407) and Tween 80 (T 80) were used to formulate GS-loaded cubosomes (GS-CBS) utilizing a melt dispersion-emulsification technique. GS-CBS were characterized by testing particle diameter, surface charge and entrapment efficiency. The formulations were optimized using a Box-Behnken statistical design, and the optimum formula (including GMO with a concentration of 4.9%, P 407 with a concentration of 10%, and T 80 with a concentration of 1%) was investigated for morphology, release behavior, ex vivo permeation through the nasal mucosa, and physical stability. Moreover, the optimal formula was incorporated into a thermosensitive gel and subjected to histopathological and in vivo biodistribution experiments. It demonstrated sustained release characteristics, increased ex vivo permeability and improved physical stability. Moreover, the cubosomal in situ gel was safe and biocompatible when applied to the nasal mucosa. Furthermore, compared to a drug solution, the nose-to-brain pathway enhanced bioavailability and brain distribution. Finally, the cubosomal in situ gel may be a potential nanocarrier for GS delivery to the brain through nose-to-brain pathway.

Design and optimization of ganciclovir solid dispersion for improving its bioavailability.

Development of new approaches for oral delivery of an existing antiviral drug aimed to enhance its permeability and hence bioavailability. Ganciclovir (GC) is an antiviral drug that belongs to class III in biopharmaceutical classification. The encapsulation of poorly absorbed drugs within nanosized particles offers several characteristics to drug due to their acquired surface properties. In the following study, the solvent evaporation technique was used to incorporate GC, within elegant nanosize particles using cyclodextrin and shellac polymers for enhancing its permeability and release pattern. Formulation variables were optimized using 23 full factorial design. The prepared formulations were assessed for yield, particle size, content, and micromeritics behavior. The optimized formula (F6) was identified through differential scanning calorimetry and Fourier transform infrared. In vitro release and stability were also assessed. Pharmacokinetic parameters of optimized nano GC solid dispersion particles (NGCSD-F6) were finally evaluated. The optimized formula (F6) showed a mean particle size of 288.5 ± 20.7 nm, a zeta potential of about 23.87 ± 2.27, and drug content 95.77 ± 2.1%. The in vitro drug release pattern of F6 showed an initial burst release followed by a sustained release over the next 12 h. The optimized formula showed accepted stability upon storage at room and refrigerator temperatures for 6 months with good flowing properties (Carr's index = 18.28 ± 0.44). In vivo pharmacokinetic study in rabbits revealed 2.2 fold increases in the bioavailability of GC compared with commercial convention tablets. The study affords evidence for the success of the solid dispersion technique under specified conditions in improvement of bioavailability of GC.

Synthesized nano particles of glimepiride via spray freezing into cryogenic liquid: characterization, antidiabetic activity, and bioavailability.

The aim of this work was to formulate glimepiride (class II drug) which is characterized by low solubility and high permeability as nanostructured particles using a cryogenic technique with an aid of water-soluble polymer to improve its aqueous solubility and hence its bioavailability. 27 formula of glimepiride nano size particles were prepared by a spray freezing into cryogenic liquid (SCFL) using poly vinyl pyrrolidone K-30 (PVP K-30); that three drug polymer ratio (1:1, 1:2, and 1:3), with three different volumes of feeding solution (50, 100, 150 mL), at three flow rates (10, 20, and 30 mL/min). The prepared formulations were evaluated for production yield, particle size, zeta potential, drug content, release rate, in vivo hypoglycemic activity, and bioavailability. All prepared formulations showed high production yield and drug content ranged between 91.1 ± 3.4% and 94.3 ± 1.8% and 95.1 ± 2.8% and 97.1 ± 2.5%, respectively. The mean particles size was ranged between 280 ± 62 nm and 520 ± 30 nm. The results of in vitro release study revealed significant enhancement in the solubility of prepared formulations compared with the pure drug. It was found that optimal formula showed a significant reduction in blood glucose levels in diabetic rats, and 1.79-fold enhancements in oral bioavailability compared with market tablets. Nanoparticle prepared by SCFL method is an encouraging formula for improving the solubility and the bioavailability of glimepiride.

Mini-Tablets versus Nanoparticles for Controlling the Release of Amoxicillin: In vitro/In vivo Study.

INTRODUCTION: Controlling the drug release from the dosage form at a definite rate is the main challenge for a successful oral controlled-release drug delivery system. In this study, mini-tablets (MTs) and lipid/polymer nanoparticles (LPNs) of lipid polymer and chitosan in different ratios were designed to encapsulate and control the release time of Amoxicillin (AMX). METHODS: Physical characteristics and in vitro release profiles of both MT and LPN formulations were studied. Antimicrobial activity and oral pharmacokinetics of the optimum MT and LPN formulations in comparison to market tablet were studied in rats. RESULTS: All designed formulations of AMX as MTs and LPNs showed accepted characteristics. MT-6 (Compritol/Chitosan 1:1) showed the greatest retardation among all prepared minitablet preparations, releasing about 79.5% of AMX over 8 h. In contrast, LPN-11 (AMX: Cr 1:3/Chitosan 1 mg/mL) had the slowest drug release, revealing the sustained release of 80.9% within 8 h. The MIC of both optimized tablet formula (MT-6) and LPNs formula (LPN-11) was around two-fold lower than the control against H. pylori. The Cmax of MT-6 and LPN11 were non significantly different compared with the marketed AMX product. While the bioavailability experiment proved that the relative bioavailability of the AMX was 1.85 and 1.8 after the oral use of LPN11 and MT-6, respectively, compared to the market tablet. CONCLUSION: The results verified that both controlled-release mini-tablets and lipid/polymer nanoparticles can be used for sustaining the release and hence improve the bioavailability of amoxicillin.