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 # Summary
 
-[Chi](https://chi.readthedocs.io) is an open source Python package designed for the modelling of treatment responses with support for implementation, simulation and inference. Supported treatment response models include pharmacokinetic & pharmacodynamic (PKPD) models, physiology-based pharmacokinetic (PBPK) models, quantitative systems pharmacology (QSP) models, and nonlinear mixed effects (NLME) models. The package provides two interfaces to implement single-individual treatment response models: 1. an SBML interface, which implements models based on SBML file specifications [@hucka:2003]; and 2. a general purpose interface that allows users to implement their own, custom models using Python code. Models implemented using SBML files automatically provide routines to administer dosing regimens and to evaluate parameter sensitivities using the simulation engine [Myokit](http://myokit.org/) [@clerx2016myokit]. These single-individual treatment response models can be extended to NLME models, making the simulation of inter-individual variability of treatment responses possible.
+[Chi](https://chi.readthedocs.io) is an open source Python package for treatment response modelling with support for implementation and simulation of treatment response model, as well as inference of model parameters from data. Supported treatment response models include pharmacokinetic & pharmacodynamic (PKPD) models, physiology-based pharmacokinetic (PBPK) models, quantitative systems pharmacology (QSP) models, and nonlinear mixed effects (NLME) models. The package provides two interfaces to implement single-individual treatment response models: 1. an SBML interface, which implements models based on SBML file specifications [@hucka:2003]; and 2. a general purpose interface that allows users to implement their own, custom models using Python code. Models implemented using SBML files automatically provide routines to administer dosing regimens and to evaluate parameter sensitivities using the simulation engine [Myokit](http://myokit.org/) [@clerx2016myokit]. These single-individual treatment response models can be extended to NLME models, making the simulation of inter-individual variability of treatment responses possible.
 
 In [Chi](https://chi.readthedocs.io), model parameters can be estimated from data using Bayesian inference. We provide a simple interface to estimate posterior distributions of model parameters from single-patient data or from population data. For the extreme case where the available population data only contains a single measurement for each individual, the package also implements a novel inference approach for NLME models, called filter inference, which enables the inference of NLME model parameters from snapshot time series data [@Augustin:2023]. For the purpose of model-informed precision dosing, [Chi](https://chi.readthedocs.io) can be used together with optimisation algorithms to find individual-specific dosing regimens that target desired treatment responses.