ProjektportalMachine learning applied to pharmacokinetic data
Terrmin
HT23
Beskrivning
Background: Tuberculosis (TB) is one of the top 10 causes of death and the leading cause of death from a single infectious agent [1]. After decades of limited progress, the development of novel anti-TB medicines was revitalized at the start of the 21st century due to the re-purposing of existing drugs and finding new drugs [2]. However, progress has been still slow for a disease that, until the emergence of the COVID-19 pandemic, has remained the top infectious killer worldwide. Therefore, WHO announces the new goal to reach the ambitious target of ‘‘ending TB’’ set by the WHO within its new Global Strategy (2016–2030) [3].
In order to speed up the process of TB research, people start to move their eyes to apply Artificial intelligence (AI) in pharmacometrics. AI has shown its power in pre-clinical discovery, like compound selection or molecular optimization [4]. It also has made its way into drug development. Commonly, pharmacometrics is used for the analysis and prediction of drug pharmacokinetics (PK), but can be time- and labor-intensive. AI might be a method enabling faster analysis of PK data.
Aim: To explore the ML methods for handling missing PK information and investigate the interpolation and extrapolation ability of ML for PK.
Method: You will be working with “NONMEM”, a software for nonlinear mixed-effects modeling, “R” for the generation of graphs and statistics and ”Python” for AI model building. Data will be simulated using population PK models implemented in NONMEM. AI algorithms will be tested and rebuilt by fitting the simulated data. Accuracy, RMSE, and graphical analysis will be used to evaluate the optimal PK sampling strategy for data analysis using AI.
References:
1. Mello FCQ, Silva DR, Dalcolmo MP. Tuberculosis: where are we?. J Bras Pneumol. 2018;44(2):82. doi:10.1590/s1806-37562017000000450
2. Guy ES, Mallampalli A. Managing TB in the 21st century: existing and novel drug therapies. Ther Adv Respir Dis. 2008 Dec;2(6):401-8. doi: 10.1177/1753465808099522. PMID: 19124385.
3. Floyd K, Glaziou P, Houben RMGJ, Sumner T, White RG, Raviglione M. Global tuberculosis targets and milestones set for 2016-2035: definition and rationale. Int J Tuberc Lung Dis. 2018;22(7):723-730. doi:10.5588/ijtld.17.0835
4. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021;26(1):80-93. doi:10.1016/j.drudis.2020.10.010
Huvudområde
Farmaceutisk vetenskap
Ämne
Farmakometri
Typ
Beräkningsstudie
Företag
Uppsala University
Ort/Plats
Uppsala
Handledarens namn
Huifang You and Ulrika Simonsson
Handledarens e-post
huifang.you@farmbio.uu.se
Institution
Institutionen för farmaceutisk biovetenskap
Program
Masterprogram i läkemedelsmodellering
Kurs
Degree Project in Pharmaceutical Modeling within Pharmacometrics 45 c - 3FB029
Omfattning/hp
45hp
Hur många studenter kan antagas för detta projekt?
1