Novel dual-target FAAH and TRPV1 ligands as potential pharmacotherapeutics for pain management.

Dual-acting drugs that simultaneously inhibit fatty acid amide hydrolase (FAAH) and antagonize the transient receptor potential vanilloid 1 (TRPV1) is a promising stronger therapeutic approach for pain management without side effects associated with single-target agents. Here, several series of dual FAAH/TRPV1 blockers were designed and synthesized through rational molecular hybridization between the pharmacophore of classical TRPV1 antagonists and FAAH inhibitors. The studies resulted in compound 2r, which exhibited strong dual FAAH/TRPV1 inhibition/antagonism in vitro, exerted powerful analgesic effects in formalin-induced pain test (phase II, in mice), desirable anti-inflammatory activity in carrageenan-induced paw edema in rats, no TRPV1-related hyperthermia side effect, and favorable pharmacokinetic properties. Meanwhile, the contributions of TRPV1 and FAAH to its antinociceptive effects were verified by target engagement and molecular docking studies. Overall, compound 2r can serve as a new scaffold for developing FAAH/TRPV1 dual-activie ligands to counteract pain.

Design, synthesis and biological evaluation of small molecule fluorescent probes targeting EGFR for tumor detection and treatment.

The epidermal growth factor receptor (EGFR) is a tyrosine kinase receptor that plays a crucial role in cell differentiation and tumor progression, and its overexpression is closely associated with the development and metastasis of multiple cancers. The development of a fluorescent probe capable of targeting EGFR while simultaneously integrating diagnostic and therapeutic functions could have a profound impact on the treatment of related cancers. In this study, we developed a series of EGFR-targeting probes that consisted of an environment-sensitive 1,8-naphthalimide fluorophore, a linker unit and a targeting unit (gefitinib), using a coupling strategy. The synthesized probes were first evaluated for their spectroscopic properties and cytotoxicities against different cell lines, which were selected based on their intrinsic EGFR expression levels. Remarkably, among the probes tested, GP1 showed outstanding environmental sensitivity and exhibited a specific response to tumor cells that overexpress EGFR. Furthermore, the representative probe GP1 was evaluated for its EGFR-specific targeting ability in live-cell fluorescence imaging and in vivo xenograft imaging, as well as its in vivo anti-tumor activity. The results showed that the probe GP1 had excellent EGFR-specific targeting ability, exhibited competitive replacement behavior towards the EGFR inhibitor gefitinib, and demonstrated potent anti-tumor effects in a CT-26 tumor-bearing mouse model. Overall, as a turn-on EGFR targeting fluorescent ligand, GP1 holds immense promise as a valuable tool for tumor detection and treatment.

Novel dual-target µ­opioid and TRPV1 ligands as potential pharmacotherapeutics for pain management.

Currently, the development of effective analgesic drugs with few side effects remains a great challenge. Studies have suggested that multi-target drug treatments show high efficacy and reduced side effects compared to single-target drug therapies. In this work, we designed and synthesized two series of novel MOR/TRPV1 dual active ligands in which the phenylpiperidine group or the N-phenyl-N-(piperidin-4-yl) propionamide group as the MOR pharmacophore was fused to the benzylpiperazinyl urea-based TRPV1 pharmacophore. In particular, compound 5a exhibited promising dual pharmacological activity for MOR (EC50 = 53.7 nM) and TRPV1 (IC50 = 32.9 nM) in vitro. In formalin tests, compound 5a showed potent, dose-dependent in vivo analgesic activity in both the 1st and 2nd phases. Gratifyingly, compound 5a did not cause the side effects of hyperthermia and analgesic tolerance. Consistent with its in vitro activity, compound 5a also simultaneously agonized MOR and antagonized TRPV1 in vivo. Further studies on compound 5a showed acceptable pharmacokinetic properties and brain permeability. Furthermore, molecular docking studies showed that compound 5a tightly bound to the active pockets of hMOR and hTRPV1, respectively. Overall, this work shows the promise in discovering new analgesic treatments through the strategy of simultaneously targeting MOR and TRPV1 with a single molecule.

Rational design synthesis and evaluation of a novel near-infrared fluorescent probe for selective imaging of amyloid-ß aggregates in Alzheimer's disease.

Alzheimer's disease (AD) is a degenerative neurological disorder that remains incurable to date, seriously affecting the quality of life and health of those affected. One of the key neuropathological hallmarks of AD is the formation of amyloid-ß (Aß) plaques. Near-infrared (NIR) probes that possess a large Stokes shift show great potential for imaging of Aß plaques in vivo and in vitro. Herein, we proposed a rational strategy for design and synthesis of a series of NIR fluorescent probes that incorporate a tricarbonitrile group as a strong electron-withdrawing group (EWG) to enable NIR emission and large Stokes shift for optimal imaging of Aß plaques. The probe TCM-UM exhibited remarkable in vitro performance, including strong NIR emission (λem = 670 nm), large Stokes shift (120-245 nm), and its affinity for Aß42 aggregates (Kd = 43.78 ± 4.09 nM) was superior to the commercially available probe Thioflavin T (ThT, Kd = 896.04 ± 33.43 nM). Further, TCM-UM was selected for imaging Aß plaques in brain tissue slices and APP/PS1 transgenic (AD) mice, the results indicated that TCM-UM had an excellent ability to penetrate the blood-brain barrier (BBB) compared with ThT, and it could effectively distinguish wild-type (Wt) mice and APP/PS1 transgenic (AD) mice.

Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network.

To generate and evaluate post-therapeutic optical coherence tomography (OCT) images based on pre-therapeutic images with generative adversarial network (GAN) to predict the short-term response of patients with retinal vein occlusion (RVO) to anti-vascular endothelial growth factor (anti-VEGF) therapy. Real-world imaging data were retrospectively collected from 1 May 2017, to 1 June 2021. A total of 515 pairs of pre-and post-therapeutic OCT images of patients with RVO were included in the training set, while 68 pre-and post-therapeutic OCT images were included in the validation set. A pix2pixHD method was adopted to predict post-therapeutic OCT images in RVO patients after anti-VEGF therapy. The quality and similarity of synthetic OCT images were evaluated by screening and evaluation experiments. We quantitatively and qualitatively assessed the prognostic accuracy of the synthetic post-therapeutic OCT images. The post-therapeutic OCT images generated by the pix2pixHD algorithm were comparable to the actual images in edema resorption response. Retinal specialists found most synthetic images (62/68) difficult to differentiate from the real ones. The mean absolute error (MAE) of the central macular thickness (CMT) between the synthetic and real OCT images was 26.33 ± 15.81 µm. There was no statistical difference in CMT between the synthetic and the real images. In this retrospective study, the application of the pix2pixHD algorithm objectively predicted the short-term response of each patient to anti-VEGF therapy based on OCT images with high accuracy, suggestive of its clinical value, especially for screening patients with relatively poor prognosis and potentially guiding clinical treatment. Importantly, our artificial intelligence-based prediction approach's non-invasiveness, repeatability, and cost-effectiveness can improve compliance and follow-up management of this patient population.

Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images.

PURPOSE: To generate and evaluate individualized post-therapeutic optical coherence tomography (OCT) images that could predict the short-term response of anti-vascular endothelial growth factor (VEGF) therapy for diabetic macular edema (DME) based on pre-therapeutic images using generative adversarial network (GAN). METHODS: Real-world imaging data were collected at the Department of Ophthalmology, Qilu Hospital. A total of 561 pairs of pre-therapeutic and post-therapeutic OCT images of patients with DME were retrospectively included in the training set, 71 pre-therapeutic OCT images were included in the validation set, and their corresponding post-therapeutic OCT images were used to evaluate the synthetic images. A pix2pixHD method was adopted to predict post-therapeutic OCT images in DME patients that received anti-VEGF therapy. The quality and similarity of synthetic OCT images were evaluated independently by a screening experiment and an evaluation experiment. RESULTS: The post-therapeutic OCT images generated by the GAN model based on big data were comparable to the actual images, and the response of edema resorption was also close to the ground truth. Most synthetic images (65/71) were difficult to differentiate from the actual OCT images by retinal specialists. The mean absolute error (MAE) of the central macular thickness (CMT) between the synthetic OCT images and the actual images was 24.51 ± 18.56 µm. CONCLUSIONS: The application of GAN can objectively demonstrate the individual short-term response of anti-VEGF therapy one month in advance based on OCT images with high accuracy, which could potentially help to improve treatment compliance of DME patients, identify patients who are not responding well to treatment and optimize the treatment program.

Prediction of Visual Acuity after anti-VEGF Therapy in Diabetic Macular Edema by Machine Learning.

Purpose: To predict visual acuity (VA) 1 month after anti-vascular endothelial growth factor (VEGF) therapy in patients with diabetic macular edema (DME) by using machine learning. Methods: This retrospective study included 281 eyes with DME receiving intravitreal anti-VEGF treatment from January 1, 2019, to April 1, 2021. Eighteen features from electronic medical records and measurements data from OCT images were extracted. The data obtained from January 1, 2019, to November 1, 2020, were used as the training set; the data obtained from November 1, 2020, to April 1, 2021, were used as the validation set. Six different machine learning algorithms were used to predict VA in patients after anti-VEGF therapy. After the initial detailed investigation, we designed an optimization model for convenient application. The VA predicted by machine learning was compared with the ground truth. Results: The ensemble algorithm (linear regression + random forest regressor) performed best in VA and VA variance predictions. In the validation set, the mean absolute errors (MAEs) of VA predictions were 0.137-0.153 logMAR (within 7-8 letters), and the mean square errors (MSEs) were 0.033-0.045 logMAR (within 2-3 letters) for the 1-month VA predictions, respectively. For the prediction of VA variance at 1 month, the MAEs were 0.164-0.169 logMAR (within 9 letters), and the MSEs were 0.056-0.059 logMAR (within 3 letters), respectively. Conclusions: Our machine learning models could accurately predict VA and VA variance in DME patients receiving anti-VEGF therapy 1 month after, which would be much valuable to guide precise individualized interventions and manage expectations in clinical practice.