Large Process Models: Business Process Management in the Age of Generative AI

The continued success of Large Language Models (LLMs) and other generative artificial intelligence approaches highlights the advantages that large information corpora can have over rigidly defined symbolic models, but also serves as a proof-point of the challenges that purely statistics-based approaches have in terms of safety and trustworthiness. As a framework for contextualizing the potential, as well as the limitations of LLMs and other foundation model-based technologies, we propose the concept of a Large Process Model (LPM) that combines the correlation power of LLMs with the analytical precision and reliability of knowledge-based systems and automated reasoning approaches. LPMs are envisioned to directly utilize the wealth of process management experience that experts have accumulated, as well as process performance data of organizations with diverse characteristics, e.g., regarding size, region, or industry. In this vision, the proposed LPM would allow organizations to receive context-specific (tailored) process and other business models, analytical deep-dives, and improvement recommendations. As such, they would allow to substantially decrease the time and effort required for business transformation, while also allowing for deeper, more impactful, and more actionable insights than previously possible. We argue that implementing an LPM is feasible, but also highlight limitations and research challenges that need to be solved to implement particular aspects of the LPM vision

Noncanonical SQSTM1/p62-Nrf2 pathway activation mediates proteasome inhibitor resistance in multiple myeloma cells via redox, metabolic and translational reprogramming.

Multiple Myeloma (MM) is a B-cell malignancy characterized by the accumulation of clonal plasma cells in the bone marrow, with drug resistance being a major cause of therapeutic failure. We established a carfilzomib-resistant derivative of the LP-1 MM cell line (LP-1/Cfz) and found that the transcription factor NF-E2 p45-related factor 2 (Nrf2; gene symbol NFE2L2) contributes to carfilzomib resistance. The mechanism of Nrf2 activation involved enhanced translation of Nrf2 as well as its positive regulator, the autophagy receptor sequestosome 1 (SQSTM1)/p62. The eukaryotic translation initiation factor gene EIF4E3 was among the Nrf2 target genes upregulated in LP-1/Cfz cells, suggesting existence of a positive feedback loop. In line with this, we found that siRNA knockdown of eIF4E3 decreased Nrf2 protein levels. On the other hand, elevated SQSTM1/p62 levels were due at least in part to activation of the PERK-eIF2α pathway. LP-1/Cfz cells had decreased levels of reactive oxygen species as well as elevated levels of fatty acid oxidation and prosurvival autophagy. Genetic and pharmacologic inhibition of the Nrf2-EIF4E3 axis or the PERK-eIF2α pathway, disruption of redox homeostasis or inhibition of fatty acid oxidation or autophagy conferred sensitivity to carfilzomib. Our findings were supported by clinical data where increased EIF4E3 expression was predictive of Nrf2 target gene upregulation in a subgroup of patients with chemoresistant minimal residual disease and relapsed/refractory MM. Thus, our data offer a preclinical rationale for including inhibitors of the SQSTM1/p62-Nrf2 pathway to the treatment regimens for certain advanced stage MM patients