diff --git a/.gitignore b/.gitignore index ba2ec833b..dd2ca8655 100644 --- a/.gitignore +++ b/.gitignore @@ -15,4 +15,7 @@ inst/shiny/log/* .DS_Store # {shinytest2}: Ignore new debug snapshots for `$expect_values()` *_.new.png -desktop.ini \ No newline at end of file +desktop.ini +/inst/suggestions_* +/inst/suggestions.html +/.quarto/ \ No newline at end of file diff --git a/vignettes/background.Rmd b/vignettes/background.Rmd index f63953232..c0ee7ddfa 100644 --- a/vignettes/background.Rmd +++ b/vignettes/background.Rmd @@ -12,13 +12,12 @@ vignette: > # Background: Understanding Non-Compartmental Analysis (NCA) and the Role of aNCA This document will help you understand the fundamentals of Non-Compartmental Analysis (NCA) in pharmacokinetics (PK) and the challenges analysts face when performing NCA. It also explains why the aNCA application was developed and how it aims to streamline and enhance the NCA workflow. For a video-content referring to all these concepts you can also visit the [R Pharma aNCA video](https://www.youtube.com/watch?v=RW1zbnDkTCM). -This document will help you understand the fundamentals of Non-Compartmental Analysis (NCA) in pharmacokinetics (PK) and the challenges analysts face when performing NCA. It also explains why the aNCA application was developed and how it aims to streamline and enhance the NCA workflow. ## What is Pharmacokinetics and Why Does NCA Matter? -Drug development is not just about studying how drugs act on the body (*pharmacodynamics*); it’s equally important to understand how the body interacts with drugs. This is what we call **pharmacokinetics (PK)**—the study of how drugs are absorbed, distributed, metabolized, and excreted over time. +Drug development is not just studying how drugs act on the body (*pharmacodynamics*); it’s equally important to understand how the body interacts with drugs. This is what we call **pharmacokinetics (PK)**—the study of how drugs are absorbed, distributed, metabolized, and excreted over time. -To visualize this, we use PK curves: graphs showing drug concentration plotted against time after dosing. These curves tell us how long a drug stays in the patient's system, helping us determine the optimal dose that balances efficacy and safety. The sweet spot—where drug levels are high enough to have an effect but not so high as to cause toxicity—is called the **therapeutic window**. +To visualize this, we use **PK curves**: graphs showing drug concentration plotted against time after dosing. These curves tell us how long a drug stays in the patient's system, helping us determine the optimal dose that balances efficacy and safety. The sweet spot—where drug levels are high enough to have an effect but not so high as to cause toxicity—is called the **therapeutic window**. **One of the main goals of PK analysis is to calculate the dose that achieves this window for the longest and safest period.** @@ -26,9 +25,9 @@ To visualize this, we use PK curves: graphs showing drug concentration plotted a ## What Is Non-Compartmental Analysis (NCA)? -When it comes to evaluating PK data, analysis approaches can range from simple to highly complex. On one end, you have **compartmental models**, where drug movement between body compartments (like blood, liver, or muscle) is described mathematically using differential equations. On the other end, you have **Non-Compartmental Analysis (NCA)**, which simplifies the process by treating the body as a "black box." +When it comes to evaluating PK data, analysis approaches can range from simple to highly complex. On one end, **compartmental models**, treats the body as discrete compartments (like blood, liver, or muscle), and models the drug flow mathematically using differential equations. On the other end, you have **Non-Compartmental Analysis (NCA)**, which simplifies the process by treating the body as a "black box." -In NCA, instead of modeling how a drug moves between compartments, we focus only on the *macroparameters*—high-level PK metrics that describe overall drug behavior. These include: +In NCA, instead of modeling how a drug moves between compartments, we focus only on the *macroparameters*—high-level PK metrics that describe overall drug behavior. These include: \ - **AUC (Area Under the Curve)**: A measure of total drug exposure over time. - **Cmax**: The peak concentration of the drug in the blood. - **Tmax**: The time it takes to reach peak concentration. @@ -44,7 +43,7 @@ Because NCA calculations are **model-independent**, they make fewer assumptions - AUC represents the total drug exposure in the body. - It’s calculated by summing up the areas under the concentration-time curve using methods like the trapezoidal rule. - Two common AUC calculations are: - - **AUClast**: Exposure up to the last measurable concentration (the one before concentrations falling below quantifiable levels, BLQ). + - **AUClast**: Exposure up to the last measurable concentration (the one before concentrations falling below quantifiable levels, **BLQ**). - **AUCinf**: Total exposure to infinity, including extrapolated data beyond the last measurable point (requires estimating a slope from the terminal phase). ### **2. Half-Life ($t_{1/2}$):** @@ -58,6 +57,9 @@ Because NCA calculations are **model-independent**, they make fewer assumptions - **Clearance and Volume of Distribution:** Derived from AUC and half-life, these parameters help understand how efficiently the body eliminates the drug and how widely it distributes. --- +(Add: historically was messy, ad-hoc process. This new workflow is mostly done silently by software, but now requires strict adherence to standards.) + +(Suggestion: Simple diagram to show flow of data and glossary of new terms.) ## Challenges and Common Actions in NCA Analysis @@ -82,14 +84,14 @@ Despite its mathematical simplicity, **performing NCA can be operationally compl ## Why Do We Need aNCA? -Despite its simplicity, NCA workflows often become a bottleneck in drug development due to inefficiencies and heavy manual interventions: -- Fragmented workflows require coordination between multiple roles (data collectors, programmers, analysts). -- Many NCA tools are proprietary, expensive, and lack flexibility for custom needs. +Despite its simplicity, NCA workflows often become a bottleneck in drug development due to inefficiencies and heavy manual interventions: \ +- Fragmented workflows require coordination between multiple roles (data collectors, programmers, analysts). \ +- Many NCA tools are proprietary, expensive, and lack flexibility for custom needs. \ - Errors introduced during manual data handling lead to reproducibility challenges and delays. -This is why we created **aNCA**: -- To simplify and unify the entire NCA process, from data preparation to generating CDISC-compliant outputs. -- To empower analysts, even those without extensive programming experience, by offering a **user-friendly interface** built on **PKNCA**, an open-source R package for NCA in pharmacokinetics. +This is why we created **aNCA**: \ +- To simplify and unify the entire NCA process, from data preparation to generating CDISC-compliant outputs.\ +- To empower analysts, even those without extensive programming experience, by offering a **user-friendly interface** built on **PKNCA**, an open-source R package for NCA in pharmacokinetics.\ - To increase automation and reduce errors, ensuring a faster and reproducible workflow. ## Do you want to know more?