DNA nanoball sequencing (DNBSEQ) is one of the most rapidly developing sequencing technologies and is widely applied in genomic and transcriptomic investigations. Recently, a new PE300 sequencing option primarily recommended for amplicon analysis was released for DNBSEQ-G99 and G400 devices. Given their unprecedentedly high data yield per flow cell, the new PE300 kits could be a great choice for various sequencing tasks, but we found that combining different types of DNA libraries in a single run could lead to undesired artifacts in the data. In this study, we investigate the occasional read cross-contamination that we first observed in our DNBSEQ PE300 run. The phenomenon, which we refer to as “software contamination”, is not actual contamination but primarily manifests as improper forward/reverse read pairing, improper demultiplexing, or as “digital chimeric” reads. Although rare, these artifacts were found in all runs we have analyzed, including several MGI demo datasets (both PE100 and PE150). In this study, we demonstrate that these artifacts arise primarily from the incorrect resolution of sequencing signals produced by neighboring DNA nanoballs, leading to mixing out forward and reverse reads or improper demultiplexing. The artifacts occur most frequently with read pairs where the length of insert sequence is shorter than the read length. Based on a few external NA12878 human exome sequencing data, we conclude that the total improper pairing rate in DNBSEQ data is comparable to Illumina ones. Overall, the problem only affects the analysis results when simultaneously sequenced libraries have markedly different insert size distribution or flow cell loading. Additionally, we demonstrate here that raw DNBSEQ data might contain ~2% optical duplicates, resulting from the same effect of close neighboring of DNB-sites in the flow cell.

Canagliflozin is an SGLT2 inhibitor prescribed for the management of type 2 diabetes mellitus, lowering blood glucose by increasing urinary glucose excretion (UGE). However, pharmacokinetic (PK) and pharmacodynamic (PD) responses vary across patient populations, complicating dose selection under altered organ function. Here, we developed a whole-body PBPK/PD digital twin of canagliflozin that integrates absorption, distribution, metabolism, and excretion, and explicitly models renal glucose handling. The model represents canagliflozin and its major metabolites (M5, M7, M9) and was calibrated and evaluated using curated PK/PD data from 22 clinical studies spanning healthy individuals, patients with type 2 diabetes, and cohorts with renal or hepatic impairment. Simulations reproduced dose-dependent exposure and UGE across single- and multiple-dose regimens. Renal impairment produced modest changes in parent drug exposure but markedly reduced UGE and increased metabolite exposure, consistent with reduced glucose filtration and impaired metabolite clearance. Under hepatic impairment, simulations predicted increased canagliflozin exposure with altered metabolite profiles, while PD effects were minimal, although evaluation was limited by sparse clinical PD endpoints. All model files, simulation scripts, and curated datasets are provided in open SBML workflows in accordance with FAIR principles, enabling reproducible simulations and reuse for model-informed analyses of canagliflozin PK/PD variability.

Background/Objectives: Empagliflozin is an SGLT2 inhibitor prescribed for the management of type 2 diabetes mellitus, lowering blood glucose by increasing urinary glucose excretion (UGE) through inhibition of renal glucose reabsorption. PK/PD responses vary substantially across patient populations, complicating dose selection under altered organ function. Here, we developed a whole-body PBPK/PD digital twin integrating absorption, distribution, metabolism, and excretion with explicit modeling of renal glucose handling via the renal threshold for glucose. Methods: The model represents empagliflozin and its glucuronide metabolite, is implemented in SBML, and was calibrated and evaluated against curated PK/PD data from 27 clinical studies spanning healthy individuals, patients with type 2 diabetes, and cohorts with renal or hepatic impairment. Results: The model accurately captured observed clinical PK/PD data across all 27 studies, spanning a wide range of doses, dosing regimens, and patient populations. Good agreement between simulations and observations was obtained under normal and impaired renal and hepatic function, as well as under fasted and fed conditions, demonstrating the model's ability to reproduce empagliflozin disposition and pharmacodynamic response across clinically relevant scenarios. Conclusions: This SBML-based PBPK/PD digital twin provides quantitative insight into empagliflozin dose dependency and the impact of renal impairment, hepatic impairment, and food intake on PK/PD across clinically relevant populations. All model files, simulation scripts, and curated datasets are openly available in accordance with FAIR principles.