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X-ray fluorescence (XRF) spectrometry has been used for detecting binding events and measuring binding selectivities between chemicals and receptors. XRF may also be used for estimating the therapeutic index of a chemical. For estimating the binding selectivities of a chemical versus chemical analogs, for measuring post translational modification of proteins, and for drug manufacturing.
We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. Here, the framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.
We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure --- this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.
We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.
We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure - this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.
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Protease inhibitors are the most potent antivirals against HIV-1, but they still lose efficacy against resistant variants. Improving the resistance profile is key to developing more robust inhibitors, which may be promising candidates for simplified next-generation antiretroviral therapies. Here, in this study, we explored analogs of darunavir with a P1 phosphonate modification in combination with increasing size of the P1′ hydrophobic group and various P2′ moieties to improve potency against resistant variants. The phosphonate moiety substantially improved potency against highly mutated and resistant HIV-1 protease variants, but only when combined with more hydrophobic moieties at the P1′ and P2′ positions. Phosphonate analogs with a larger hydrophobic P1′ moiety maintained excellent antiviral potency against a panel of highly resistant HIV-1 variants, with significantly improved resistance profiles. The cocrystal structures indicate that the phosphonate moiety makes extensive hydrophobic interactions with the protease, especially with the flap residues. Many residues involved in these protease-inhibitor interactions are conserved, enabling the inhibitors to maintain potency against highly resistant variants. These results highlight the need to balance inhibitor physicochemical properties by simultaneous modification of chemical groups to further improve resistance profiles.
During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors, this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. Here, the results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.
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Tumor cell apoptosis evasion is one of the main reasons for easy metastasis occurrence, chemotherapy resistance, and the low five-year survival rate of digestive system tumors. Current research has shown that non-apoptotic cell death plays an important role in tumors of the digestive system. Therefore, increasing the proportion of non-apoptotic tumor cells is one of the effective methods of improving therapeutic efficacies for digestive system tumors. Non-apoptotic cell death modes mainly include autophagic cell death, pyroptosis, ferroptosis, in addition to other cell death modes. This review covers a systematic review relating to the research progress made into autophagic cell death, pyroptosis, ferroptosis, and other cell death modes in the treatment of digestive system tumors. It also highlights how treatment is a reasonable prospect based on clinical experience and provides reliable guidance for the further development of digestive system tumor treatments.
Interest in the use of 225 Ac for targeted alpha therapies has increased dramatically over the past few years, resulting in a multitude of new isotope production and translational research efforts. However, 225 Ac radioimmunoconjugate (RIC) research is still in its infancy, with most prior experience in hematologic malignancies and only one reported preclinical solid tumor study using 225 Ac RICs. In an effort to compare 225 Ac RICs to other current antibody conjugates, a variety of RICs are tested against intractable small-cell lung cancer (SCLC). Here we directly compare, in vitro and in vivo , two promising candidates of each α or β - category, 225 Ac and 177 Lu, versus pyrrolobenzodiazepine (PBD) nonradioactive benchmarks. The monoclonal antibody constructs are targeted to either delta like 3 protein (DLL3), a recently discovered SCLC target, or CD46 as a positive control. An immunocompromised maximum tolerated dose assay is performed on NOD SCID mice, along with tumor efficacy proof-of-concept studies in vivo . We overview the conjugation techniques required to create serum-stable RICs and characterize and compare in vitro cell killing with RICs conjugated to nonspecific antibodies (huIgG1) with either native or site-specific thiol loci against tumor antigen DLL3-expressing and nonexpressing cell lines. Using patient-derived xenografts of SCLC onto NOD SCID mice, solid tumor growth was controlled throughout 3 weeks before growth appeared, in comparison to PBD conjugate controls. NOD SCID mice showed lengthened survival using 225 Ac compared to 177 Lu RICs, and PBD dimers showed full tumor suppression with nine out of ten mice. The exploration of RICs on a variety of antibody-antigen systems is necessary to direct efforts in cancer research toward promising candidates. However, the anti-DLL3-RIC system with 225 Ac and 177 Lu appears to be not as effective as the anti-DLL3-PBD counterpart in SCLC therapy with matched antibodies and portrays the challenges in both SCLC therapy as well as the specialized utility of RICs in cancer treatment.