Current Pharmaceutical Design - Volume 27, Issue 17, 2021

Liposomes are nano-sized formulations having the benefits of site-specificity, biocompatibility, and biodegradability, which make them useful for the therapy and diagnosis of major diseases like cancer. In this review, various synthetic strategies of liposomes and their biomedical application in special concern to cancer are discussed. In context to the biomedical application, this article gives a detailed insight into subcellular targeted therapy and several therapeutic modifications like immunotherapy, receptor-based therapy, phototherapy, and combination therapy. The review also describes the liposome-based imaging platforms and the toxicity associated with liposomes. Owing to a significant amount of benefits of this carrier system, several products have been approved to be launched in the market and several others have already been marketed for clinical use.

Lymphatic system is the secondary circulation system of the human body after the systemic circulation. Various problems, including the first-pass metabolism through oral administration of medicines, can be resolved by lymphatic targeting. Lymphatic absorption has been explored in detail, and studies reveal the improved bioavailability of medicines. In the case of cancer, AIDS, and various other health problems, lymphatic targeting has been focused on due to the fact that lymph nodes are involved greatly in tumor metastasis. This article reviews lymphatic absorption and its exploration in the treatment of various health problems. The physiology of the lymphatic system, the mechanisms of absorption, and the various formulation systems suitable for lymphatic absorption have been discussed. Some recent novel approaches like hydrodynamically driven device (HDD) and carbon nanotubes for lymphatic delivery have also been appraised.

Nitric oxide (NO) is a promising pharmaceutical component that has vasodilator, anti-bacterial, and wound healing activities. Chronic ulcers are non-healing disorders that are generally associated with distortion of lower limbs. Among the severe consequence derivatives of these diseases are the problems of chronic wound progression. NO, which is categorized as the smallest gaseous neurotransmitter, has beneficial effects in different phases of chronic inflammation. The defensive mechanism of NO is found useful in several severe conditions, such as gestational healing, gastrointestinal healing, and diabetic healing. The current review presents an updated collection of literature about the role of NO in chronic ulcers due to the prevalence of diabetes, DPN, and diabetic foot ulcers, and because of the lack of available effective treatments to directly address the pathology contributing to these conditions, novel treatments are being sought. This review also collects information about deficiency of NO synthase in diabetic patients, leading to a lack of vascularization of the peripheral nerves, which causes diabetic neuropathy, and this could be treated with vasodilators such as nitric oxide. Apart from the pharmacological mechanism of NO, the article also reviewed and analyzed to elucidate the potential of transdermal delivery of NO for the treatment of chronic ulcers.

Background: Buccal drug delivery is a fascinating research field. Gel-based formulations present potent characteristics as buccal systems since they have great physicochemical properties. Methods: Among the various gels, in situ gels are viscous colloidal systems consisting of polymers; when physiological conditions change (pH, temperature, ion activation), they are transformed into the gel phase. These systems can improve bioavailability. Other systems, such as nanogels or emulgels can also be applied for buccal delivery with promising results. Polymeric gel-based systems can be produced by natural, semisynthetic, and synthetic polymers. Their main advantage is that the active molecules can be released in a sustained and controllable manner. Several gels based on chitosan are produced for the entrapment of drugs demonstrating efficient retention time and bioavailability due to chitosan mucoadhesion. Besides polysaccharides, poloxamers and carbopol are also used in buccal gels due to their high swelling ability and reversed thermal gelation behavior. Results: Herein, the authors focused on the current development of mucoadhesive gel systems used in buccal drug delivery. After explaining buccal drug delivery and mucoadhesion, various studies with hydrogels, in situ gels, and nanogels were analyzed as buccal gel systems. Various mucoadhesive gel studies with mucoadhesive polymers have been studied and summarized. This review is presented as valuable guidance to scientists in formulating buccal mucoadhesive drug delivery systems. Conclusion: This review aimed to assist researchers working on buccal drug delivery by summarizing buccal drug delivery, mucoadhesion, and buccal mucoadhesive gel systems recently found in the literature.

Mycobacterium tuberculosis, because of its unique biochemical behavior and a complex host relationship, successfully evades the host immune system. Therefore, chemotherapy appears to be the first-line option for patients with tuberculosis. However, poor patient compliance with anti-tubercular treatment and variability in anti-tubercular drug pharmacokinetics are among the major driving factors for the emergence of drug resistance. The rising cases of extrapulmonary TB, cross-resistance patterns, high prevalence of tuberculosis and HIV co-infections make tuberculosis treatment more complicated than conventional multidrug therapy. Due to their distinct advantages like higher solubility, increased payload, controlled release profiles, tissue-specific accumulation, and lack of toxicity, nanoscale materials have immense potential for drug delivery applications. An appropriate selection of polymer and careful particle engineering further improves therapeutic outcomes with opportunities to overcome conventional anti-tubercular drugs' challenges. The present review introduces the prospect of using nanotechnology in tuberculosis (TB) chemotherapy and provides a comprehensive overview of recent advances in nanocarriers implied for delivering anti-tubercular drugs.

Background and Introduction: Peripheral neuropathy is one of the most common dose-limiting side effects of solvent-based paclitaxel. Paclitaxel poliglumex (PPX) and NK105 were developed to overcome the paclitaxel induced peripheral neuropathy. However, the incidence of peripheral neuropathy induced by PPX and NK105 was reported higher than solvent-based paclitaxel, but evidence remains inconsistent. Methods: The article was reported in accordance with PRISMA Guidelines (Registration number: CRD42021245313). We conducted a meta-analysis to compare the incidence and severity of peripheral neuropathy between solvent-based paclitaxel, PPX and NK105 mono-chemotherapy. Results: Results revealed that no significant difference exists between the incidence of all grade peripheral neuropathy among the solvent-based paclitaxel, PPX and NK105 treated groups. While, the incidence of high grade peripheral neuropathy induced by NK105 was lower than two other groups. Moreover, the overall survival was not improved in PPX compared with other groups. However, NK105 demonstrated significant longer overall survival in patients with cancer. Conclusion: Current evidence suggests more attention should be paid to the paclitaxel poliglumex re-formulation.

Aim: A systematic review which aims to assess the evidence regarding the function of the autonomic heart rate regulation system among Parkinson’s disease (PD) patients. The main objective of the study is to compare heart rate variability (HRV) between those with and without PD from published studies. The subgroup analyses aimed to investigate the impact of treatment and disease duration on heart rate variability (HRV), assessed by measuring sympathetic and parasympathetic activity via low-frequency (LF) and high-frequency (HF) power spectrum scores, in patients with Parkinson’s disease (PD). Methods: PubMed, Cochrane Library, Embase and Web of Science were searched using the keywords “Parkinson’s disease” and “heart rate variability”. Studies that reported at least one HRV variable were included. The quality of the included studies was evaluated, and the relevant information was extracted. A meta-analysis was carried out with Stata software. We followed the PRISMA guidelines for all stages of the meta-analysis. PROSPERO registration number: CRD42021242766. Results: Thirteen references (16 studies) were included in our analysis. The LF values (g -0.27; 95% confidence interval (CI) -0.53 to -0.01) of the patients with PD were lower than the controls. No significant differences in HF values (g -0.11; 95% CI -0.28 to 0.06) were observed between groups. Subgroup analyses of HRV outcomes in patients stratified by treatment status and disease duration were performed. For LF, patients with a disease duration of less than 5 years presented lower HF (g -0.25; 95% CI -0.44 to -0.06) values than controls. Regarding HF, patients receiving treatment presented lower HF (g -0.22; 95% CI -0.40 to 0.05) values than controls, and patients with a disease duration greater than 5 years also presented lower HF (g -0.29; 95% CI -0.56 to -0.03) values than controls. Discussion: We have confirmed and elaborated on the hypothesis of sympathovagal imbalance in PD. Knowledge of the effect of sympathovagal balance on HRV may inform the design of therapeutic regimens for PD. However, between-study heterogeneity and methodological issues limit the generalizability of the evidence; thus, future studies employing strict methodologies are warranted. Conclusion: Our meta-analysis found that PD is associated with reduced HRV values, which indicates that both sympathetic and vagal activities are decreased. Patients in the early stage of PD have sympathetic autonomic nerve dysfunction, with only minor damage to sympathetic activity.

Internet of Things (IoT) emerges as disruptive innovation and development in the fields of drug delivery and biomedical sciences using on-target active transportation, sensors, wearable devices, real-time diagnostics, etc. Semiconducting fluorescence emitting material, quantum dots on integration with IoT displayed interesting results in the healthcare sector, especially in hospitals and pathological laboratories. Presently, the integrated system is used to improve productivity without the interference of human and offer a cost-effective system. This integrated system can be used for the detection of various diseases like epilepsy, cancer, diabetes, etc., and various biomedical applications like energy storage, lights, sensor technology, light filters, etc. The integrated technology is implemented into the field of medicine for simplifying the approaches in therapeutics and diagnostic applications. The collected and analyzed data are further useful for healthcare professionals to find patient-centric solutions. Artificial Intelligence-aided IoT emerges as a novel technology for transmitting and securing health data. Despite some of the limitations like e-waste and the risk of hacking, an IoT-based QD system will be considered as a modern healthcare provider with life-saving products for enriching the medical quality and real-time accessibility.

Background: Drug-Target interactions are vital for drug design and drug repositioning. However, traditional lab experiments are both expensive and time-consuming. Various computational methods which applied machine learning techniques performed efficiently and effectively in the field. Results: The machine learning methods can be divided into three categories basically: Supervised methods, Semi-Supervised methods and Unsupervised methods. We reviewed recent representative methods applying machine learning techniques of each category in DTIs and summarized a brief list of databases frequently used in drug discovery. In addition, we compared the advantages and limitations of these methods in each category. Conclusion: Every prediction model has both strengths and weaknesses and should be adopted in proper ways. Three major problems in DTIs prediction including the lack of nonreactive drug-target pairs data sets, over optimistic results due to the biases and the exploiting of regression models on DTIs prediction should be seriously considered.

Deep learning, an emerging field of artificial intelligence based on neural networks in machine learning, has been applied in various fields and is highly valued. Herein, we mainly review several mainstream architectures in deep learning, including deep neural networks, convolutional neural networks and recurrent neural networks in the field of drug discovery. The applications of these architectures in molecular de novo design, property prediction, biomedical imaging and synthetic planning have also been explored. Apart from that, we further discuss the future direction of the deep learning approaches and the main challenges we need to address.