Accepted Article

Huang ZH and You T. (2021) Personalise Vitamin D3 Using Physiologically-Based Pharmacokinetic Modelling. CPT: Pharmacometrics & Systems Pharmacology. PDF

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Download model in R, training data and test data in the zip archive in Supporting Information

 

App: https://beyond.shinyapps.io/vitamind3/

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Why Vitamin D ?

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Vitamin D is important

• Vitamin D is essential for intestinal calcium absorption and bone health.

• Vitamin D may play potential roles in the prevention of osteoporosis, cancer, diabetics, autoimmune disease and COVID-19 (Benskin, 2020)

 

Vitamin D deficiency is common all around the world

• Target: serum 25(OH)D level ≥75 nmol/L (25(OH)D is an active metabolite of vitamin D)

       Severely deficient:            < 30 nmol/L           (FNB & IOM, 1997)

       Deficient:                           30 – 49 nmol/L      (IOM, 2011)

       Insufficient:                       50 – 74 nmol/L      (Holick, 2007)

       Target for prevention:      ≥ 75 nmol/L           (Holick, 2007)

       Danger of toxicity:            > 250 nmol/L

• In North America, the prevalence of severe vitamin D deficiency increased to 10% in 2001-2006: the elderly, pregnant women, the black community and obese population accounted for a large proportion (Ganji et al, 2012)

• In Europe, vitamin D deficiency in most countries were over 20%, except some Nordic countries: traditional diet of cod and cod liver in Nordic regions is speculated to explain such difference (Feldman et al, 2018)

• In the UK, National Dietary and Nutrition Survey shows higher prevalence of hypovitaminosis D (marked by serum 25(OH)D < 40nmol/L) in the north of UK than the south, and the prevalence of hypovitaminosis D in most regions are higher than 30% in spring (Hyppönen et al, 2007)

  • Serum 25(OH)D was poor in the elderly

  • Low serum 25(OH)D has also been observed in UK adolescents

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What are the main challenges?

 

Guidelines for daily vitamin D intake issued by different countries are different

• US: Recommended Dietary Allowance (RDA) proposed by Institute of Medicine (IOM) (Ross et al, 2011)

  • Target levels: 25(OH)D ≥ 50 nmol/L

  • 600 IU/d for 1 to 70 years old

  • 800 IU/d for 71 years old and over

• UK:  Reference Nutrient Intake (RNI) proposed by Scientific Advisory Committee on Nutrition (SACN, 2016) 

  • Target levels: 25(OH)D ≥ 25 nmol/L

  • 400 IU per day all year round for the general UK population, including pregnant and lactating women and people at increased risk of vitamin D deficiency

 

No model is accurate enough to help one effectively achieve sufficiency with the right dose and schedule

• Many clinical trials have been performed all over the world, yet data not readily available

• No model can accurately predict vitamin D pharmacokinetics in a person

  • Some models are poorly calibrated and unreliable: overfitted with many implicit assumptions not supported by evidence (Sawyer et al, 2015)

  • A nonlinear mixed effects model could only recapitulate limited doses and could not generate reliable prediction for an individual: the modelling attributes differences among individuals to random errors and do not attempt to understand the differences to make accurate predictions

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What have we done?

 

We made high quality data readily available

• We found over 100 trials from peer-reviewed publications as credible sources of information: the majority was randomised control trials to guarantee quality

• We put in huge efforts to digitise the graphs to tabulate the values for modelling 

• We filled in the missing metadata in line with with the best practice to curate the most comprehensive, up-to-date and high quality dataset that characterise vitamin D pharmacokinetics (PK)

We built a novel physiologically-based pharmacokinetics (PBPK) model that is truly predictive

• Our data exploration highlighted baseline 25(OH)D serum levels were connected with PK

• We only used a subset of data to identify the right structure and right parameters of a model​

• Our PBPK model most economically represented a simple hypothesis for this observation

• This model accurately predicted 25(OH)D PK at doses far higher than the training set (i.e. >2500 µg)

Our work will help millions achieve vitamin D sufficiency

• Our modelling made it possible to predict unique PK in an individual for the first time

• A serological test in conjunction with our modelling may enable personalise vitamin D

  • Assess suitability of any dose regimen for helping an individual achieve optimal vitamin D

  • Estimate the time it takes to reach target 25(OH)D level in order to schedule follow up blood test

  • Predict 25(OH)D levels in real world situations that involve missing doses and drug holidays to ensure compliance

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Want to access our data?

 

Data set we compiled are available for academic and commercial purposes.

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Write us an email and let us know (* marks required information)

• Your full name *

• Company * | Department * | Job title *

• Email *

• Phone 

• Why you need to access the data *

• How you plan to use the data *

• Will your activity contribute to The Open Project *

 

If your email meets all requirements, we will schedule an online meeting to discuss the way forward.

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