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Abstract Mobocertinib is an oral tyrosine kinase inhibitor approved for treatment of patients with locally advanced or metastatic non‐small cell lung cancer (mNSCLC) with epidermal growth factor receptor gene (EGFR) exon 20 insertion mutations whose disease has progressed on or after platinum‐based chemotherapy. This population pharmacokinetic (PK) analysis describes the PK of mobocertinib and its active metabolites, AP32960, and AP32914, using data from two phase I studies in healthy volunteers (n = 110) and two phase I/II studies in patients with mNSCLC (n = 317), including the pivotal phase I/II study. The plasma PK of mobocertinib, AP32960, and AP32914 were well‐characterized by a joint semimechanistic model that included two compartments for mobocertinib with absorption via three transit compartments, two compartments for AP32960, and one compartment for AP32914. The observed time‐dependency in PK was described by an enzyme compartment with drug and metabolite concentration‐dependent stimulation of enzyme production, resulting in the enzyme increasing the apparent clearance of mobocertinib, AP32960, and AP32914. Effects of healthy volunteer status (vs. patients with mNSCLC) on apparent oral clearance of all three moieties and on apparent central volume of distribution for mobocertinib were included as structural covariates in the final model. No clinically meaningful differences in mobocertinib PK were observed based on age (18–86 years), race, sex, body weight (37.3–132 kg), mild‐to‐moderate renal impairment (estimated glomerular filtration rate 30–89 ml/min/1.73 m2 by modification of diet in renal disease equation), or mild‐to‐moderate hepatic impairment, suggesting that no dose adjustment is required based on these covariates in patients with mNSCLC.

Local energy markets are emerging as a tool for coordinating generation, storage, and consumption of energy from distributed resources. In combination with automation, they promise to provide an effective energy management framework that is fair and brings system-level savings. The cooperative–competitive nature of energy markets calls for multi-agent based automation with learning energy trading agents. However, depending on the dynamics of the agent–environment interaction, this approach may yield unintended behavior of market participants. Thus, the design of market mechanisms suitable for reinforcement learning agents must take into account this interplay. This article introduces autonomous local energy exchange (ALEX) as an experimental framework that combines multi-agent learning and double auction mechanism. Participants determine their internal price signals and make energy management decisions through market interactions, rather than relying on predetermined external price signals. The main contribution of this article is examination of compatibility between specific market elements and independent learning agents. Effects of different market properties are evaluated through simulation experiments, and the results are used for determine a suitable market design. The results show that market truthfulness maintains demand-response functionality, while weak budget balancing provides a strong reinforcement signal for the learning agents. The resulting agent behavior is compared with two baselines: net billing and time-of-use rates. The ALEX-based pricing is more responsive to fluctuations in the community net load compared to the time-of-use. The more accurate accounting of renewable energy usage reduced bills by a median 38.8% compared to net billing, confirming the ability to better facilitate demand response.

TLR7 and TLR8 are key members of the Toll-like receptor family, playing crucial roles in the signaling pathways of innate immunity, and thus become attractive therapeutic targets of many diseases including infections and cancer. Although TLR7 and TLR8 show a high degree of sequence homology, their biological response to small molecule binding is very different. Aiming to understand the mechanism of selective profiles of small molecule modulators against TLR7 and TLR8, we carried out molecular dynamic simulations on three imidazoquinoline derivatives bound to the receptors separately. They are Resiquimod (R), Hybrid-2 (H), and Gardiquimod (G), selective agonists of TLR7 and TLR8. Our MD trajectories indicated that in the complex of TLR7-R and TLR7-G, the two chains forming the TLR7 dimer tended to remain "open" conformation, while the rest systems maintained in the closed format. The agonists R, H, and G developed conformational deviation mainly on the aliphatic tail. Furthermore, we attempted to quantify the selectivity between TLR7 and TLR8 by binding free energies via MM-GBSA method. It showed that the three selected modulators were more favorable for TLR7 than TLR8, and the ranking from the strongest to the weakest was H, R and G, aligning well with experimental data. In the TLR7, the flexible and hydrophobic aliphatic side chain of H has stronger van der Waals interactions with V381 and F351 but only pick up interaction with one amino acid residue i.e. Y353 of TLR8. Unsurprisingly, the positively charged side chain of G has less favorable interaction with I585 of TLR7 and V573 of TLR8 explaining G is weak agonist of both TLR7 and TLR8. All three imidazoquinoline derivatives can form stable hydrogen bonds with D555 of TLR7 and the corresponding D543 of TLR8. In brief, the set of total 400ns MD studies sheds light on the potential selectivity mechanisms of agonists towards TLR7 and TLR8, indicating the van der Waals interaction as the driving force for the agonists binding, thus provides us insights for designing more potent and selective modulators to cooperate with the hydrophobic nature of the binding pocket.

The activity of a key mitochondrial tricarboxylic acid cycle enzyme, α-ketoglutarate dehydrogenase complex (KGDHC), declines in many neurodegenerative diseases. KGDHC consists of three subunits. The dihydrolipoyl succinyl transferase (DLST) component is unique to KGDHC. DLST+/− mice showed reduced mRNA and protein levels and decreased brain mitochondrial KGDHC activity. Neurotoxic effects of mitochondrial toxins were exacerbated in DLST+/− mice. MPTP produced a significantly greater reduction of striatal dopamine and tyrosine hydroxylase-positive neurons in the substantia nigra pars compacta of DLST+/− mice. DLST deficiency enhanced the severity of lipid peroxidation in the substantia nigra after MPTP treatment. Striatal lesions induced by either malonate or 3-nitropropionic acid (3-NP) were significantly larger in DLST+/− mice than in wildtype controls. DLST deficiency enhanced the 3-NP inhibition of mitochondria enzymes, and 3-NP induced protein and DNA oxidations. These observations support the hypothesis that reductions in KGDHC may impair the adaptability of the brain and contribute to the pathogenesis of neurodegenerative diseases.