In the context of Pharma 4.0, i.e., the pharmaceutical version of Industry 4.0, pharmaceutical quality control (PQC) for a pharmaceutical cyber-physical system (PCPS) plays a critical role in ensuring the quality of drug products during pharmaceutical development. However, drug customization through Pharma 4.0 also introduces uncertainty embodied in ever-changing critical material attributes, which presents new challenges related to development costs and efficiency in PQC compared to traditional control modes. Although we have proposed a data-driven methodology to tackle these challenges, it ignores some visible or potential process knowledge that also contains much additional information reflecting the laws and trends governing pharmaceutical process operations. This not only sacrifices the opportunity to use this knowledge to make up for insufficiencies in the information provided by the data but also goes against the core ideology of future intelligent manufacturing. In this article, we introduce the idea of a data- and knowledge-driven approach into PQC for the first time by proposing a general data- and knowledge-driven adaptive PQC framework for a PCPS-based two phases—PQC by direct data- and knowledge-driven adaptive iterative learning control and PQC by learning from primitive data and knowledge. Next, a case study is presented to preliminarily investigate the application of the proposed framework in a simulated pharmaceutical spray fluidized bed granulation process. Finally, a series of simulation experiments are designed to verify the feasibility and effectiveness of the proposed framework.

Alzheimer’s disease (AD) is a chronic, progressive neurodegenerative condition that constitutes the predominant form of dementia, encompassing 60% to 80% of cases. AD manifests with an insidious onset; its evolution is nonreversible once initiated. The preclinical phase can span multiple decades;¹ consequently, many patients are already in the intermediate to advanced stages at the point of clinical diagnosis. Presently, there remains an absence of established therapeutic strategies.¹ The etiopathogenesis of AD is intricate, with multiple prevailing theories regarding its causative factors and fundamental mechanisms, yet a consensus remains elusive. In recent two decades, the amyloid-$\beta$ ($\mathrm{A}\beta$) toxicity paradigm has garnered widespread endorsement within the scientific realm. Intriguingly, a mere few years before identifying $\mathrm{A}\beta$ protein variants, certain researchers posited that cerebral microangiopathy and inflammatory processes might be intrinsic initiators of AD.^{2 – 5} Within the pathophysiological framework of AD, deep cerebral microvasculature assumes an incrementally critical function. The microvasculature not only orchestrates the provision of essential nutrients and oxygen to cerebral tissues but also oversees the evacuation of metabolic waste products. Anomalies in vascular structures, particularly in cerebral regions, are postulated to have a direct nexus with AD pathogenesis.⁶ In our previous study, we reported the direct effects of cerebrospinal fluid-$\mathrm{A}\beta$ on cerebrovascular morphology and brain tissue.⁷ Due to the technique limitation, microvasculature change of the deep cortex was not evaluated specifically.

In the context of Pharma 4.0, pharmaceutical quality control (PQC) is beset by issues such as uncertainties from ever-changing critical material attributes and strong coupling between variables in the multi-unit pharmaceutical tablet manufacturing process (PTMP), and how to timely adjust the operational variables to deal with such challenges has become a key problem in PQC. In this study, we propose a novel data-knowledge-driven modeling and operational adjustment framework for PTMP by integrating Bayesian network (BN) and case-based reasoning (CBR). At the modeling level, first, a distributed concept is introduced, i.e., the BN model for each subunit of PTMP is established in accordance with the operation process sequence, and the transition variables are given by the BN model established first and retrieved as the new query for the next unit. Once the BN models of all subunits are built, they are integrated into a global BN model. At the operational adjustment level, by taking the expected critical quality attributes (CQAs) and related prior information as evidence, the operational adjustment is achieved through global BN reasoning. Finally, the case study in a sprayed fluidized-bed granulation-based PTMP demonstrates the feasibility and effectiveness in improving the terminal CQAs of the proposed method, which is also compared with other methods to showcase its efficacy and merits.

Marine-derived fungi are assuming an increasingly central role in the search for natural leading compounds with unique chemical structures and diverse pharmacological properties. However, some gene clusters are not expressed under laboratory conditions. In this study, we have found that a marine-derived fungus Aspergillus sp. SYPUF29 would survive well by adding an exogenous nitric oxide donor (sodium nitroprusside, SNP) and nitric oxide synthetase inhibitor (L-NG-nitroarginine methyl ester, L-NAME) in culture conditions. Moreover, using the LC-MS/MS, we initially assessed and characterized the difference in metabolites of Aspergillus sp. SYPUF29 with or without an additional source of nitrogen. We have found that the metabolic pathway of Arginine and proline metabolism pathways was highly enriched, which was conducive to the accumulation of alkaloids and nitrogen-containing compounds after adding an additional source of nitrogen in the cultivated condition. Additionally, the in vitro anti-neuroinflammatory study showed that the extracts after SNP and L-NAME were administrated can potently inhibit LPS-induced NO-releasing of BV2 cells with lower IC₅₀ value than without nitric oxide. Further Western blotting assays have demonstrated that the mechanism of these extracts was associated with the TLR4 signaling pathway. Additionally, the chemical investigation was conducted and led to nine compounds (SF1–SF9) from AS1; and six of them belonged to alkaloids and nitrogen-containing compounds (SF1–SF6), of which SF1, SF2, and SF8 exhibited stronger activities than the positive control, and showed potential to develop the inhibitors of neuroinflammation.