AI and Rare Diseases ── 2026-10-21
New papers on rare diseases (other than neurological and cancer) and AI, collected from journal feeds, PubMed (searched across all journals) and OpenAlex (a public index used instead of Google Scholar), judged by Jev (an AI that makes language judgements): original research, AI central, which of 8 disease groups, treatment or trials, and then ranked by importance up to a daily limit. Journals are not filtered by impact factor; each paper shows its journal's IF-equivalent (OpenAlex two-year mean citedness) and the date of that value. Each entry shows the full original abstract.
Read by disease: Platforms（1） · Inborn errors of metabolism（0） · Genetic syndromes（0） · Rare blood disorders（0） · Immune and connective tissue（0） · Heart and lung（0） · Kidney, liver and gut（0） · Skin and bone（0）
Rare-disease diagnosis and drug discovery platforms and AI
- Abstract
PyDarwin is an open-source Python package integrating machine learning with NONMEM for automated model selection; however, its utility in model-informed precision dosing (MIPD) remains unvalidated. This study aims to evaluate pyDarwin for population pharmacokinetics (PopPK) model development in MIPD compared with expert-developed models, using paediatric tobramycin as a representative case. The model-building dataset included 450 plasma concentrations from 63 children with cystic fibrosis (CF). One group manually developed a model using stepwise covariate selection, while another applied pyDarwin within a predefined 27-dimensional model space, using a fitness function that incorporated a penalty based on 24-h area under the curve (AUC 24h ). External validation was performed using 1011 concentrations from 144 children, including evaluation using all therapeutic drug monitoring (TDM) data and clinically relevant MIPD scenarios (a priori [AP], Bayesian 1, Bayesian 2). Both models included weight-based scaling, a maturation function, and estimated glomerular filtration rate as covariates on clearance, yielding comparable clearance estimates. The pyDarwin-selected model was a two-compartment model with fixed allometric scaling exponents, whereas the manual model was a one-compartment model with estimated exponents. Both models showed comparable probability of target attainment for steady-state AUC 24h . In external validation using all TDM data, the pyDarwin-selected model demonstrated better predictive performance for CF patients than manual model (prediction-based MPE -14.6% vs -41.6%; MAPE 34.0% vs 54.4%). In MIPD scenarios, the pyDarwin-selected model showed superior performance in the AP setting, with differences diminishing after Bayesian updating. These findings support pyDarwin-based automated PopPK model selection as a reliable and efficient framework for MIPD.
Journal IF-equivalent: 3.7 (OpenAlex 2-year mean citedness, value as of 2026-10-20, retrieved 2026-10-21; not the official Clarivate IF)Reference: Wang J, Harwood K, Yang W, Lei A, Lai T, Nieforth K, et al. Predictive Performance of pyDarwin-Selected Versus Expert-Developed Paediatric Tobramycin Population Pharmacokinetic Models for Model-Informed Precision Dosing. AAPS J. 2026 Oct 19;28(6):171. doi:10.1208/s12248-026-01321-z.Checked: Abstract only
Inborn errors of metabolism: treatment, trials and AI
No new qualifying paper for this issue.
Genetic syndromes and paediatric rare diseases: treatment, trials and AI
No new qualifying paper for this issue.
Rare blood disorders: treatment, trials and AI
No new qualifying paper for this issue.
Autoinflammatory, immunodeficiency and rare connective-tissue diseases: treatment, trials and AI
No new qualifying paper for this issue.
Rare cardiovascular and respiratory diseases: treatment, trials and AI
No new qualifying paper for this issue.
Rare kidney, liver and gastrointestinal diseases: treatment, trials and AI
No new qualifying paper for this issue.
Rare skin and bone diseases: treatment, trials and AI
No new qualifying paper for this issue.