Mathematical Modeling of Complement Pathway Dynamics for Target Validation and Selection of Drug Modalities for Complement Therapies

Artificial intelligence-driven rational design of ionizable lipids for mRNA delivery

Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial intelligence (AI) and virtual screening to facilitate the rational design of ionizable lipids by predicting two key properties of LNPs, apparent pKa and mRNA delivery efficiency. Nearly 20 million ionizable lipids were evaluated through two iterations of AI-driven generation and screening, yielding three and six new molecules, respectively. In mouse test validation, one lipid from the initial iteration, featuring a benzene ring, demonstrated performance comparable to the control DLin-MC3-DMA (MC3). Notably, all six lipids from the second iteration equaled or outperformed MC3, with one exhibiting efficacy akin to a superior control lipid SM-102. Furthermore, the AI model is interpretable in structure-activity relationships.

Assessing the performance of QSP models: biology as the driver for validation

Validation of a quantitative model is a critical step in establishing confidence in the model's suitability for whatever analysis it was designed. While processes for validation are well-established in the statistical sciences, the field of quantitative systems pharmacology (QSP) has taken a more piecemeal approach to defining and demonstrating validation. Although classical statistical methods can be used in a QSP context, proper validation of a mechanistic systems model requires a more nuanced approach to what precisely is being validated, and what role said validation plays in the larger context of the analysis. In this review, we summarize current thoughts of QSP validation in the scientific community, contrast the aims of statistical validation from several contexts (including inference, pharmacometrics analysis, and machine learning) with the challenges faced in QSP analysis, and use examples from published QSP models to define different stages or levels of validation, any of which may be sufficient depending on the context at hand.

Low-molecular weight inhibitors of the alternative complement pathway

Dysregulation of the alternative complement pathway predisposes individuals to a number of diseases. It can either be evoked by genetic alterations in or by stabilizing antibodies to important pathway components and typically leads to severe diseases such as paroxysmal nocturnal hemoglobinuria, atypical hemolytic uremic syndrome, C3 glomerulopathy, and age-related macular degeneration. In addition, the alternative pathway may also be involved in many other diseases where its amplifying function for all complement pathways might play a role. To identify specific alternative pathway inhibitors that qualify as therapeutics for these diseases, drug discovery efforts have focused on the two central proteases of the pathway, factor B and factor D. Although drug discovery has been challenging for a number of reasons, potent and selective low-molecular weight (LMW) oral inhibitors have now been discovered for both proteases and several molecules are in clinical development for multiple complement-mediated diseases. While the clinical development of these inhibitors initially focuses on diseases with systemic and/or peripheral tissue complement activation, the availability of LMW inhibitors may also open up the prospect of inhibiting complement in the central nervous system where its activation may also play an important role in several neurodegenerative diseases.