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Lixoft Monolix Suite 2019 R2 x64 一款適用於藥物開發的建模和仿真軟件
Lixoft Monolix是基於藥物模型開發參考的平台。它結合了最先進的算法具有獨特的易用性。藥物計量
學家和生物統計學習可以依靠Monolix進行生物分析,並去模型PK/PD和其它複雜的生化和生理過程。Monolix是
一種簡單,快捷,功能強大的工具,適用於參數估計的非線性混合效應模型,模型診斷和評估,以及先進的圖形表示等。
Monolix is the most advanced and simple solution for non-linear mixed effects modeling (NLME) for pharmacometrics. It is based on the SAEM algorithm and provides robust, global convergence even for complex PK/PD models. Monolix is used for preclinical and clinical population PK/PD modeling and for Systems Pharmacology. Monolix enjoys a large user community. Monolix is widely used by academia, the pharmaceutical industry as well as the US regulatory agencies.

Advanced Statistical Methodologies
Reliable convergence for all type of data is a centerpiece in population PKPD modeling, which is why Lixoft pioneered in collaboration with Inria the SAEM algorithm.

Automated generation of diagnostic tests
Monolix automatically generates a full set of diagnostic plots even for complex PK/PD models. For example, you can instantaneously create the Visual Predictive Check, split by any patient subgroup you would want to investigate.

Increased productivity and quality
Efficient C++ solver package, standardized model language with Mlxtran, PK/PD model library and integrated software all contribute to better productivity and quality.

Very easy to use with its GUI
Our solutions are designed for ease of use. Monolix can be used via a graphical interface or command lines for powerful scripting. This means less programming for you and more focus on exploring models and pharmacology to deliver in time to your customers.

Key features:
Support of all relevant data types and statistical features
Monolix covers a wide range of data types and statistical features for population PK/PD modeling. For all cases the right statistical methodology has been developed and published for reference.

Monolix covers:
Continuous, categorical, count and repeated time to event data.
Mixture models and mixtures of models.
Inter-occasion variability with any number of levels.
Proper handling of BLQ data.
Normal, lognormal, logit, probit and user defined distributions for the individual parameters.
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