Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug.

Due to its cost-effectiveness, convenience, and high patient adherence, oral drug administration normally remains the preferred approach. Yet, the effective delivery of hydrophobic drugs via the oral route is often hindered by their limited water solubility and first-pass metabolism. To mitigate these challenges, advanced delivery systems such as solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been developed to encapsulate hydrophobic drugs and enhance their bioavailability. However, traditional design methodologies for these complex formulations often present intricate challenges because they are restricted to a relatively narrow design space. Here, we present a data-driven approach for the accelerated design of SLNs/NLCs encapsulating a model hydrophobic drug, cannabidiol, that combines experimental automation and machine learning. A small subset of formulations, comprising 10% of all formulations in the design space, was prepared in-house, leveraging miniaturized experimental automation to improve throughput and decrease the quantity of drug and materials required. Machine learning models were then trained on the data generated from these formulations and used to predict properties of all SLNs/NLCs within this design space (i.e., 1215 formulations). Notably, formulations predicted to be high-performers via this approach were confirmed to significantly enhance the solubility of the drug by up to 3000-fold and prevented degradation of drug. Moreover, the high-performance formulations significantly enhanced the oral bioavailability of the drug compared to both its free form and an over-the-counter version. Furthermore, this bioavailability matched that of a formulation equivalent in composition to the FDA-approved product, Epidiolex®.

Revolutionizing drug formulation development: The increasing impact of machine learning.

Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic demonstrated the impact of formulation science. Yet, the design of advanced pharmaceutical formulations is non-trivial and primarily relies on costly and time-consuming wet-lab experimentation. In 2021, our group published a review article focused on the use of ML as a means to accelerate drug formulation development. Since then, the field has witnessed significant growth and progress, reflected by an increasing number of studies published in this area. This updated review summarizes the current state of ML directed drug formulation development, introduces advanced ML techniques that have been implemented in formulation design and shares the progress on making self-driving laboratories a reality. Furthermore, this review highlights several future applications of ML yet to be fully exploited to advance drug formulation research and development.

A novel 25-ferroptosis-related gene signature for the prognosis of gliomas.

Background: Ferroptosis is closely associated with cancer and is of great importance in the immune evasion of cancer. However, the relationship between ferroptosis and glioma is unclear. Methods: We downloaded the expression profiles and clinical data of glioma from the GlioVis database and obtained the expression profiles of ferroptosis genes. A ferroptosis-related gene signature was developed for the prognosis of gliomas. Results: We screened out prognostic ferroptosis genes, named ferroptosis-related genes, by the Cox regression method. Based on these genes, we used unsupervised clustering to obtain two different clusters; the principal component analysis algorithm was applied to determine the gene score of each patient, and then all the patients were classified into two subgroups. Results showed that there exist obvious differences in survival between different clusters and different gene score subgroups. The prognostic model constructed by the 25 ferroptosis-related genes was then evaluated to predict the clinicopathological features of immune activity in gliomas. Conclusion: The ferroptosis-related genes play an important role in the malignant process of gliomas, potentially contributing to the development of prognostic stratification and treatment strategies.

Self-driving laboratories: A paradigm shift in nanomedicine development.

Nanomedicines have transformed promising therapeutic agents into clinically approved medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA vaccines against COVID-19, which were made possible by lipid nanoparticle technology. Despite the success of nanomedicines to date, their design remains far from trivial, in part due to the complexity associated with their preclinical development. Herein, we propose a nanomedicine materials acceleration platform (NanoMAP) to streamline the preclinical development of these formulations. NanoMAP combines high-throughput experimentation with state-of-the-art advances in artificial intelligence (including active learning and few-shot learning) as well as a web-based application for data sharing. The deployment of NanoMAP requires interdisciplinary collaboration between leading figures in drug delivery and artificial intelligence to enable this data-driven design approach. The proposed approach will not only expedite the development of next-generation nanomedicines but also encourage participation of the pharmaceutical science community in a large data curation initiative.

Data evaluating triamcinolone acetonide and triamcinolone hexacetonide loaded poly(δ-valerolactone-co-allyl-δ-valerolactone) microparticles.

Advanced drug delivery strategies can be used to enhance the therapeutic effectiveness of locally delivered corticosteroids. Poly(δ-valerolactone-co-allyl-δ-valerolactone) microparticles (PVL-co-PAVL MPs) were evaluated for delivery of two corticosteroids, triamcinolone acetonide and triamcinolone hexacetonide. PVL-co-PAVL MPs were prepared using a modified oil-in-water emulsification method, followed by a UV-initiated cross-linking process. The resulting PVL-co-PAVL MPs were purified with an excess amount of water and then acetone to remove residual surfactant, cross-linker, and catalyst before lyophilization. Triamcinolone acetonide and triamcinolone hexacetonide were independently loaded into the resulting PVL-co-PAVL MPs via a post-loading swelling-equilibrium method. The drug-loaded MPs were characterized in terms of drug loading (determined by high-performance liquid chromatography, HPLC), thermal properties (determined by differential scanning calorimetry, DSC), and in vitro drug release kinetics (with quantification of drug using HPLC) to better understand the suitability of PVL-co-PAVL MPs for delivery of corticosteroids. These data demonstrate the potential of PVL-co-PAVL MPs as a promising drug delivery platform for the sustained release of corticosteroids. Raw data have been made available on Mendeley Data. Additional details on PVL-co-PAVL MPs were previously reported [1].

Machine learning models to accelerate the design of polymeric long-acting injectables.

Long-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. Here we show that machine learning algorithms can be used to predict experimental drug release from these advanced drug delivery systems. We also demonstrate that these trained models can be used to guide the design of new long acting injectables. The implementation of the described data-driven approach has the potential to reduce the time and cost associated with drug formulation development.

Effects of Glimepiride Combined with Recombinant Human Insulin Injection on Serum IGF-1, VEGF and TRACP-5b Oxidative Stress Levels in Patients with Type 2 Diabetes Mellitus.

Objective: This study was designed to explore the effect of glimepiride combined with recombinant human insulin injection on serum insulin-like growth factor 1 (IGF-1), vascular endothelial growth factor (VEGF), tartrate-resistant acid phosphatase 5b (TRACP-5b) and oxidative stress levels in patients with type 2 diabetes. Methods: A total of 217 patients with type 2 diabetes who were treated in our hospital from November 2018 to March 2020 were selected and divided into control group and treatment group. The control group was treated with glimepiride (n = 107). The study group was given glimepiride and recombinant human insulin injection) (n = 107). The levels of blood glucose, blood lipids, IGF-1, VEGF, TRACP-5b, and oxidative stress in the two groups were measured, respectively. We summarize the main results as follows. Insulin resistance index (HOMA-IR), fasting blood glucose (FPG), 2h postprandial blood glucose (2hBG), serum glycated hemoglobin (HbA1c), triglyceride (TG), total cholesterol (TC), serum malondialdehyde (MDA), reactive oxygen species (ROS), VEGF, and TRACP-5b levels were significantly lower than those before treatment, and the degree of reduction in the study group was greater than that in the control group (P < 0.05). The levels of insulin (INS), insulin beta cell function index (HOMA-beta), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and IGF-1 were significantly higher than those before treatment. Further, the study group demonstrated better results than the control group (P < 0.05). Conclusion: Glimepiride combined with recombinant human insulin injection can improve insulin sensitivity, reduce insulin resistance, significantly reduce glucose and lipids in patients, reduce the occurrence of oxidative stress, promote the secretion of oxidative resistance enzymes, lower the vascular endothelial growth factor (VEGF), reduced the formation of new blood vessels, and inhibit the growth and metastasis of cancer cells. Additionally, we found out that glimepiride combined with recombinant human insulin injection had a good prognosis for patients; it significantly reduced the bone resorption marker TRACP-5b and prevented the occurrence of complications such as osteoporosis. The combined use of the two is more effective than glimepiride alone. In conclusion, glimepiride combined with recombinant human insulin injection has higher application value in the treatment of patients with type 2 diabetes.

Association Between Diabetes Medications and the Risk of Parkinson's Disease: A Systematic Review and Meta-Analysis.

Background: Diabetes mellitus (DM) increases the risk of Parkinson's disease (PD). However, whether DM medications play a part on that increased PD risk is unclear. We designed this meta-analysis to assess the influence of different oral DM medications on the PD risk in patients with DM. Methods: We searched PubMed, Embase, and CENTRAL databases for relevant studies up until January 2021. We pooled adjusted outcomes to assess the PD risk in patients using different DM medications including sulfonylurea, metformin, glitazones (GTZ), dipeptidyl peptidase-4 inhibitors (DPP4i), and glucagon-like peptide-1 agonists (GLP1a). Results: We included 10 studies in our analysis. Our results indicate a lack of significant association between the PD risk and the use of sulfonylureas (three studies; HR, 1.26; 95% CI, 0.95 to 1.66; I 2, 70%; p = 0.11), DPP4i (three studies; HR, 0.69; 95% CI, 0.35 to 1.38; I 2, 88%; p = 0.30), metformin (five studies; HR, 1.23; 95% CI, 0.98 to 1.78; I 2, 84%; p = 0.13), and GTZ (six studies; HR, 0.88; 95% CI, 0.66 to 1.16; I 2, 92%; p = 0.35). After exclusion of a single study in the GTZ analysis, our results indicate a significantly reduced PD risk with GTZ use (HR, 0.78; 95% CI, 0.65 to 0.93; I 2, 59%; p = 0.06). Similarly, after the exclusion of a single study, our results indicate a significantly increased PD risk with the use of metformin (HR, 1.50; 95% CI, 1.11 to 2.02; I 2, 80%; p = 0.008). We also found a significantly reduced PD risk with the use of GLP1a (two studies; HR, 0.41; 95% CI, 0.19 to 0.87; I 2, 0%; p = 0.02). Conclusion: The role of different DM medications on the PD risk remains unclear, and the quality of studies is low. While our analysis suggests a lack of association between the use of metformin, GTZ, DPP4i, and sulfonylureas and the PD risk, metformin (to a higher degree) and GTZ may still increase the risk. Limited data suggest a protective effect of GLP1a on the PD risk.

Machine learning directed drug formulation development.

Machine learning (ML) has enabled ground-breaking advances in the healthcare and pharmaceutical sectors, from improvements in cancer diagnosis, to the identification of novel drugs and drug targets as well as protein structure prediction. Drug formulation is an essential stage in the discovery and development of new medicines. Through the design of drug formulations, pharmaceutical scientists can engineer important properties of new medicines, such as improved bioavailability and targeted delivery. The traditional approach to drug formulation development relies on iterative trial-and-error, requiring a large number of resource-intensive and time-consuming in vitro and in vivo experiments. This review introduces the basic concepts of ML-directed workflows and discusses how these tools can be used to aid in the development of various types of drug formulations. ML-directed drug formulation development offers unparalleled opportunities to fast-track development efforts, uncover new materials, innovative formulations, and generate new knowledge in drug formulation science. The review also highlights the latest artificial intelligence (AI) technologies, such as generative models, Bayesian deep learning, reinforcement learning, and self-driving laboratories, which have been gaining momentum in drug discovery and chemistry and have potential in drug formulation development.

Poly(δ-valerolactone-co-allyl-δ-valerolactone) cross-linked microparticles: Formulation, characterization and biocompatibility.

A novel polymeric material, poly(δ-valerolactone-co-allyl-δ-valerolactone) (PVL-co-PAVL), was used to manufacture microparticles (MPs) for sustained drug delivery. PVL-co-PAVL MPs were formulated using a modified oil-in-water approach, followed by a UV-initiated cross-linking process. Prepared MPs had a smooth spherical morphology and cross-linking of the copolymer was found to improve the integrity and thermal stability of the MPs. Paclitaxel (PTX) was successfully loaded into the MPs at a high drug loading capacity, using a post-loading swelling-equilibrium method. In vitro evaluation showed that the PVL-co-PAVL MPs provide sustained release of PTX, which exhibited first-order release kinetics. A subsequent pilot pharmacokinetic study was carried out on the PTX-loaded PVL-co-PAVL MPs. During this study, serum levels of PTX were monitored following subcutaneous administration of the MPs to Sprague-Dawley rats. Overall, the in vivo release of PTX from the MPs was lower than expected based on the in vitro release studies. Detectable serum levels of PTX suggest that sustained release of drug was achieved in vivo. Minimal changes in subcutaneous tissue were observed at the site of injection. Future studies will further examine the localized and systemic distribution of drug following administration in this new polymer-based MP system.

Cross-linked valerolactone copolymer implants with tailorable biodegradation, loading and in vitro release of paclitaxel.

Implantable drug delivery systems, formed from degradable and non-degradable polymers, can offer several advantages over traditional dosage forms for sustained drug delivery. The majority of degradable implant systems developed to date are composed of poly(lactide-co-glycolide) (PLGA). However, PLGA-based systems are not suitable for the delivery of all drugs. Each drug is unique in terms of physico-chemical properties, and polymer-drug compatibility plays a significant role in determining a drug formulation's performance. In this study, two novel cross-linkable δ-valerolactone-based copolymers were synthesized and used to prepare cross-linked disc-shaped implants. The manipulation of the composition of the discs and conditions used during drug loading were found to influence various aspects of the delivery system performance including the degree of swelling, degradation, drug-loading and in vitro release. The polymeric discs resulted in no adverse effects following subcutaneous implantation in naïve rats. These studies support further development of cross-linkable valerolactone matrices as implantable formulations for sustained drug delivery.

MicroRNA-185 activates PI3K/AKT signalling pathway to alleviate dopaminergic neuron damage via targeting IGF1 in Parkinson's disease.

OBJECTIVES: Studies have extensively explored the role of microRNAs (miRs) in Parkinson's disease (PD) and miR-185 is related to autophagy and apoptosis of dopaminergic neurons in PD. However, the role of miR-185 mediating insulin-like growth factor 1 (IGF1)/phosphatidylinositol-3-kinase/protein kinase B signalling pathway (PI3K/AKT) in PD still needs in-depth exploration. METHODS: Rat PD models were established by injection of 6-hydroxydopamine. PD rats were injected with miR-185 or insulin-like growth factor 1 (IGF1)-related sequences. Behaviour tests were performed, oxidative stress-related factors, tyrosine hydroxylase (TH)-, glial fibrillary acidic protein (GFAP)-, ionised calcium-binding adaptor molecule-1 (Iba-1)- and TUNEL-positive cells in the substantia nigra were determined. Levels of miR-185, IGF1 and phosphatidylinositol-3-kinase/protein kinase B (PI3K/AKT) signalling pathway-related factors were also detected. RESULTS: miR-185 level was reduced in rats with PD. Restoring miR-185 promoted behaviour functions, ameliorated pathological damages and oxidative stress, increased TH-positive dopaminergic neurons, decreased GFAP- and Iba-1-positive cells and restrained neuronal apoptosis in the substantia nigra in PD rats. miR-185 targeted IGF1 to activate PI3K/AKT signalling pathway. Up-regulation of IGF1 mitigated restored miR-185-mediated effects on PD rats. CONCLUSION: This study illustrates that miR-185 ameliorates dopaminergic neuron damage via targeting IGF1 and activating PI3K/AKT signalling pathway in PD, which renews the therapy for PD.

Advances in biosynthesis of noble metal nanoparticles mediated by photosynthetic organisms-A review.

The last decade has witnessed significant developments in the biosynthesis of noble metal nanoparticles (NMNPs) due to their distinct advantages in various practical applications. Many photosynthetic organisms, including plants, microalgae, and photosynthetic bacteria, have been explored for NMNP synthesis in an eco-friendly and cost-effective manner. These biomasses were used for NMNP biosynthesis as growing cells, non-growing cells, whole cells extract, disrupted cell extract, residual biomasses, gum solutions, etc. Different mechanisms might be involved to reduce noble metal ions to NMNP. These mechanisms include reduction of metal ions catalysed by reductases using NADH as electron donors, reduction of metal ions using biochemical molecules such as polysaccharides and proteins as electron donators, and light-dependant biosynthesis of NMNP involving pigments for light capture and water-splitting for electron supplementation. NMNP may be applied as catalyst, antibacterial, anticancer, and drug delivery vehicle.

Effects of reaction conditions on light-dependent silver nanoparticle biosynthesis mediated by cell extract of green alga Neochloris oleoabundans.

Silver nanoparticles (AgNPs) were synthesized by incubating the mixture of AgNO3 solution and whole-cell aqueous extracts (WCAEs) of Neochloris oleoabundans under light conditions. By conducting single-factor and multi-factor optimization, the effects of parameters including AgNO3 concentration, pH, and extraction time were quantitatively evaluated. The optimal conditions in terms of AgNP yield were found to be 0.8 mM AgNO3, pH 5, and 9-h extraction. The AgNPs thus synthesized were quasi-spherical with a mean particle diameter of 16.63 nm and exhibited decent uniformity as well as antibacterial activities, which may facilitate AgNP biosynthesis's application in the near future.

Mechanism of light-dependent biosynthesis of silver nanoparticles mediated by cell extract of Neochloris oleoabundans.

This study investigated the role of chlorophyll and light in the biosynthesis of silver nanoparticles (AgNPs) using disrupted cell aqueous extract of Neochloris oleoabundans. It was found that, while increasing sonication time increased the percentage of disrupted cells and efficiency of aqueous cell extraction, over-sonication reduced AgNPs production. AgNPs biosynthesis required illumination of white, blue, or purple light while AgNPs formation was undetectable under dark condition or illumination of orange or red light, indicating only photons of high energy levels among the photosynthetic active radiations are capable of exciting the electrons of chlorophylls to a state that is sufficient for Ag+ reduction. Chlorophylls were demonstrated to be an essential component mediating the reduction of Ag+ and results of mass balance suggest that chlorophylls need to be recycled for the reaction to complete. The ultimate electron donor was hypothesized to be water, which supplemented electrons through water splitting catalyzed by photosynthetic enzyme complexes such as photosystem II. A hypothetical reaction mechanism is proposed for the light-dependent biosynthesis of AgNPs based on systematic experimental results and literature data for the first time.

Effects of palonosetron and ondansetron on preventing nausea and vomiting after laparoscopic surgery.

Background This meta-analysis was performed to evaluate the efficacy and safety of palonosetron and ondansetron in preventing postoperative nausea and vomiting (PONV) in patients undergoing laparoscopic surgery with general anesthesia. Methods We searched for randomized controlled clinical trials in PubMed, Embase, and The Cochrane Library. Results Nine studies were enrolled in this meta-analysis and showed no statistically significant difference between palonosetron and ondansetron in the prevention of PONV in the first 24 hours after surgery (relative risk [RR], 0.62; 95% confidence interval [CI], 0.35-1.10). Palonosetron more effectively prevented vomiting at various time intervals during the first 24 hours postoperatively than did ondansetron: 0-2 hours (RR, 0.45; 95% CI, 0.26-0.78), 2-6 hours (RR, 0.74; 95% CI, 0.39-1.40), and 6-24 hours (RR, 1.20; 95% CI, 0.55-2.64). No significant differences in side effects were found between palonosetron and ondansetron (RR, 0.67; 95% CI, 0.40-1.14). Conclusion This meta-analysis demonstrated that palonosetron is not more efficacious than ondansetron in the prevention of early PONV. However, palonosetron was more efficacious than ondansetron in the prevention of vomiting after laparoscopic surgery.

Efficacy of ondansetron for spinal anesthesia during cesarean section: a meta-analysis of randomized trials.

Objective To investigate the efficacy and safety of ondansetron during cesarean section under spinal anesthesia. Methods We sought randomized controlled trials (RCTs) on ondansetron during spinal anesthesia for cesarean section in The Cochrane Library, PubMed, MEDLINE, and Web of Science from their inception to September 2016. Results Altogether, 21 RCTs were included in this study. Meta-analysis showed that the ondansetron group had a lower incidence of nausea/vomiting and bradycardia than the placebo group during cesarean section under spinal anesthesia [relative risk (RR) = 0.43, 95% confidence interval (CI) (0.36, 0.51) and RR = 0.45, 95% CI (0.26, 0.80), respectively]. There were no significant differences in the incidences of pruritus, hypotension, or shivering during cesarean section under spinal anesthesia [RR = 0.92, 95% CI (0.83, 1.02); RR = 0.72 (0.50, 1.06), 95% CI (0.50, 1.06); and RR = 0.89, 95% CI (0.71, 1.11), respectively]. Conclusion Ondansetron effectively reduces the incidences of nausea/vomiting and bradycardia under spinal anesthesia during cesarean section.

Using dezocine to prevent etomidate-induced myoclonus: a meta-analysis of randomized trials.

OBJECTIVE: This study was designed to evaluate the efficacy and safety of preinjection of dezocine in preventing etomidate-induced myoclonus. METHODS: PubMed, Embase, The Cochrane Library, and China National Knowledge Infrastructure (CNKI) were searched to collect relevant randomized controlled trials (RCTs) from inception to July 2016 on the preinjection of dezocine in preventing etomidate-induced myoclonus. Two researchers independently screened literature, extracted data, and evaluated bias risks in accordance with inclusion and exclusion criteria, and then used RevMan 5.2 to perform the meta-analysis. RESULTS: A total of six RCTs were included in this study. The meta-analysis showed that 1) the preinjection of dezocine can reduce the incidence of etomidate-induced myoclonus (relative risk [RR] =0.25, 95% CI [0.13, 0.50], P<0.0001), which is consistent with the result of subgroup analysis; 2) the preinjection of dezocine can reduce the incidence of mild, moderate, and severe myoclonus; 3) dezocine was not related to an increasing incidence of etomidate-induced dizziness and nausea (RR =2.83, 95% CI [0.66, 12.08], P=0.6); and 4) dezocine did not reduce heart rates after the administration of etomidate (mean difference =1.06, 95% CI [-4.08, 6.19], P=0.69). CONCLUSION: The preinjection of dezocine has the effect of both lowering the incidence of etomidate-induced myoclonus and easing the severity of myoclonus, but without increasing dizziness and nausea or affecting the heart rate.

Intravenous dexmedetomidine during spinal anaesthesia for caesarean section: A meta-analysis of randomized trials.

Objective To evaluate the efficacy and safety of spinal anaesthesia using dexmedetomidine for caesarean section. Methods PubMed, The Cochrane Library, and CNKI were searched for relevant literature. Results The incidence of nausea and vomiting in the dexmedetomidine group was significantly lower than that in the control group (OR = 0.21, 95% CI: 0.12-0.35, P < 0.00001). No difference was found in the incidence of pruritus between the two groups (OR = 1.21, 95% CI: 0.36-4.09, P = 0.76).The dexmedetomidine group had a higher incidence of bradycardia than did the control group (OR = 2.20, 95% CI: 1.02-4.77, P = 0.05). The incidence of shivering in the dexmedetomidine group was significantly lower than that in the control group (OR = 0.20, 95% CI: 0.13-0.32, P < 0.00001). The incidence of hypotension was not different between the two groups (OR = 0.88, 95% CI: 0.49-1.56, P = 0.65). Conclusion Dexmedetomidine can decrease the incidence of nausea, vomiting, bradycardia, and shivering with spinal anaesthesia during caesarean section.

The cytoplasmic translocation of Cx32 mediates cisplatin resistance in ovarian cancer cells.

Ovarian cancer is the most lethal gynecologic malignancy, and cisplatin is one of the first-line chemotherapeutic agents. However, acquired cisplatin resistance prevents the successful treatment of patients with ovarian cancer. Gap junction (GJ) and connexin (Cx) are closely related to tumor formation, but the relationship between cisplatin resistance and GJ or Cx are undetermined. In this study, we established the cisplatin-resistant human ovarian cancer cell line A2780-CDDP. Here we showed that cisplatin resistance was correlated to the loss of GJ and the upregulation of Cx32 expression. Enhancing GJ in A2780-CDDP cells could increase the apoptotic response to cisplatin treatment. Furthermore, although Cx32 expression was increased in A2780-CDDP cells, it was more localized to the cytoplasm rather than in the membrane, and knockdown of Cx32 in A2780-CDDP cells sensitized them to cisplatin treatment. In summary, Cx32 is involved in cisplatin resistance, and cytoplasmic Cx32 plays an important role in chemoresistance.