Qianfan Wang

Conjugation of small interfering RNA (siRNA) to an asialoglycoprotein receptor ligand derived from N-acetylgalactosamine (GalNAc) facilitates targeted delivery of the siRNA to hepatocytes in vitro and in vivo. The ligands derived from GalNAc are compatible with solid-phase oligonucleotide synthesis and deprotection conditions, with synthesis yields comparable to those of standard oligonucleotides. Subcutaneous (SC) administration of siRNA-GalNAc conjugates resulted in robust RNAi-mediated gene silencing in liver. Refinement of the siRNA chemistry achieved a 5-fold improvement in efficacy over the parent design in vivo with a median effective dose (ED50) of 1 mg/kg following a single dose. This enabled the SC administration of siRNA-GalNAc conjugates at therapeutically relevant doses and, importantly, at dose volumes of ≤1 mL. Chronic weekly dosing resulted in sustained dose-dependent gene silencing for over 9 months with no adverse effects in rodents. The optimally chemically modified siRNA-GalNAc conjugates are hepatotropic and long-acting and have the potential to treat a wide range of diseases involving liver-expressed genes.

In recent years, RNA interference (RNAi) therapeutics, most notably with lipid nanoparticle-based delivery systems, have advanced into human clinical trials. The results from these early clinical trials suggest that lipid nanoparticles (LNPs), and the novel ionizable lipids that comprise them, will be important materials in this emerging field of medicine. A persistent theme in the use of materials for biomedical applications has been the incorporation of biodegradability as a means to improve biocompatibility and/or to facilitate elimination. Therefore, the aim of this work was to further advance the LNP platform through the development of novel, next-generation lipids that combine the excellent potency of the most advanced lipids currently available with biodegradable functionality. As a representative example of this novel class of biodegradable lipids, the lipid evaluated in this work displays rapid elimination from plasma and tissues, substantially improved tolerability in preclinical studies, while maintaining in vivo potency on par with that of the most advanced lipids currently available. In recent years, RNA interference (RNAi) therapeutics, most notably with lipid nanoparticle-based delivery systems, have advanced into human clinical trials. The results from these early clinical trials suggest that lipid nanoparticles (LNPs), and the novel ionizable lipids that comprise them, will be important materials in this emerging field of medicine. A persistent theme in the use of materials for biomedical applications has been the incorporation of biodegradability as a means to improve biocompatibility and/or to facilitate elimination. Therefore, the aim of this work was to further advance the LNP platform through the development of novel, next-generation lipids that combine the excellent potency of the most advanced lipids currently available with biodegradable functionality. As a representative example of this novel class of biodegradable lipids, the lipid evaluated in this work displays rapid elimination from plasma and tissues, substantially improved tolerability in preclinical studies, while maintaining in vivo potency on par with that of the most advanced lipids currently available.

In this paper, we present a unit commitment problem with uncertain wind power output. The problem is formulated as a chance-constrained two-stage (CCTS) stochastic program. Our model ensures that, with high probability, a large portion of the wind power output at each operating hour will be utilized. The proposed model includes both the two-stage stochastic program and the chance-constrained stochastic program features. These types of problems are challenging and have never been studied together before, even though the algorithms for the two-stage stochastic program and the chance-constrained stochastic program have been recently developed separately. In this paper, a combined sample average approximation (SAA) algorithm is developed to solve the model effectively. The convergence property and the solution validation process of our proposed combined SAA algorithm is discussed and presented in the paper. Finally, computational results indicate that increasing the utilization of wind power output might increase the total power generation cost, and our experiments also verify that the proposed algorithm can solve large-scale power grid optimization problems.

Covalent attachment of a synthetic triantennary N-acetylagalactosamine (GalNAc) ligand to chemically modified siRNA has enabled asialoglycoprotein (ASGPR)-mediated targeted delivery of therapeutically active siRNAs to hepatocytes in vivo. This approach has become transformative for the delivery of RNAi therapeutics as well as other classes of investigational oligonucleotide therapeutics to the liver. For efficient functional delivery of intact drug into the desired subcellular compartment, however, it is critical that the nucleic acids are stabilized against nucleolytic degradation. Here, we compared two siRNAs of the same sequence but with different modification pattern resulting in different degrees of protection against nuclease activity. In vitro stability studies in different biological matrices show that 5'-exonuclease is the most prevalent nuclease activity in endo-lysosomal compartments and that additional stabilization in the 5'-regions of both siRNA strands significantly enhances the overall metabolic stability of GalNAc-siRNA conjugates. In good agreement with in vitro findings, the enhanced stability translated into substantially improved liver exposure, gene silencing efficacy and duration of effect in mice. Follow-up studies with a second set of conjugates targeting a different transcript confirmed the previous results, provided additional insights into kinetics of RISC loading and demonstrated excellent translation to non-human primates.

This paper proposes a two-stage robust optimization approach to solve the N- k contingency-constrained unit commitment (CCUC) problem. In our approach, both generator and transmission line contingencies are considered. Compared to the traditional approach using a given set of components as candidates for possible failures, our approach considers all possible component failure scenarios. We consider the objectives of minimizing the total generation cost under the worst-case contingency scenario and/or the total pre-contingency cost. We formulate CCUC as a two-stage robust optimization problem and develop a decomposition framework to enable tractable computation. In our framework, the master problem makes unit commitment decisions and the subproblem discovers the worst-case contingency scenarios. By using linearization techniques and duality theory, we transform the subproblem into a mixed-integer linear program (MILP). The most violated inequalities generated from the subproblem are fed back into the master problem during each iteration. Our approach guarantees a globally optimal solution in a finite number of iterations. In reported computational experiments, we test both primal and dual decomposition approaches. Our computational results verify the effectiveness of our proposed approach.