Quality

Drug-drug interaction (DDI) usually occur when a drug affects the expression or function of a metabolic enzyme or transporter. Since metabolism is the primary mechanism of elimination for most drugs, changes in the enzymes responsible for drug metabolism can lead to undesirable increases (or decreases) in the plasma concentration of drugs.

DDIs can also be predicted using in-silico modeling techniques. Physiologically based pharmacokinetick (PBPK) models are a common modeling approach used to investigate potential DDIs. This type of model is used to understand the impact of concomitant dosing of drugs and incorporates detailed data of human physiology, enzyme, and transporter abundance in each tissue. The benefit of an in-silico modeling approach is that the possibility for DDIs can be evaluated before going into clinical studies and evaluated in difficult to study patient populations such as pediatric or pregnant populations. The information gained from DDI simulations can help guide the direction of clinical DDI studies.

Investigating drug-drug interactions is essential to drug development because patients frequently use more than one medication at a time. Phameteo has experience designing and analyzing DDI studies at all phases of drug development. We work closely with our clients to find the best strategies for each program. it is possible to avoid a DDI study altogether with modeling and simulation techniques such as PBPK modeling. 

A gap analysis is the process of evaluating an asset or product development program based on various types of technical or regulatory information, as well as critical documents and data. A thorough analysis includes a systematic review of information from both the public domain and the Sponsor, enabling manufacturers to identify and mitigate gaps before submission.  

A gap analysis may be targeted on one specific strategy across multiple disciplines – CMC, nonclinical, clinical, and regulatory affairs. 

Conducting a gap analysis requires a depth of expertise and knowledge in many different areas. In order to anticipate your project’s unique needs, you need experts who have experience in regulatory submission strategy combined with a deep understanding of your product and are knowledgeable about all aspects of the project – scientific, technical, CMC, nonclinical, clinical, and regulatory affairs.  

It is important for a planning with a gap analysis can help to avoid missteps that may render data inconclusive or require additional patient recruitment and studies. This is especially important for small to mid-sized biotech companies that need to take extra steps to preserve limited resources and ensure available funding through completion. 

At the end of the day, conducting a thorough and well-executed gap analysis and using its findings to inform strategy development results in identifying the best and most appropriate regulatory approach – one that balances risk, costs and speed to approval. 

In a C-QT analysis, pharmacokinetic (PK) data (concentration of a drug over time) is time matched with ECG or EKG data to create a measure of exposure and response. The PK and ECG data is collected from clinical studies that have already been planned or completed. Our team then performs exposure-responses modeling techniques, using the amount of drug as the “exposure” and QT interval as the “response.”

Concentration-QT Services

• PK/PD modeling and simulation techniques can sometimes eliminate the need for a TQT study. Our C-QT services include.

• Planning study designs that best support C-QT analysis, Using a completed C-QT analysis to design a more efficient TQT analysis.

• Using NONMEM to generate quality C-QT analysis. Creating final submission-ready reports.

An in vitro in vivo correlation (IVIVC) is a predictive mathematical model that describes the relationship between an in vitro property of a dosage form and a relevant in vivo response. When performing IVIVC for formulation development, the in vitro property is primarily dissolution or drug release and the in vivo response is primarily a drug’s plasma concentration or the amount/rate of drug absorbed.

The FDA Guidance, “Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo Correlations,” is more than 20 years old but remains the definitive source of regulatory thinking on IVIVC. At the time of its release, the ability to accurately and precisely predict expected BA characteristics for an extended release product from its dissolution profile had been a long sought-after goal. 

The recommendations within the guidance cover IVIVC for oral, extended release drug products that are being developed for regulatory review as part of an NDA, ANDA, or AADA. IVIVC has many advantages. Not only does IVIVC provide a better understanding of the dosage form, but it provides a predictive tool that can eliminate the need for certain clinical BE studies, help in interpreting batch-to-batch variability, and help to optimize formulation development, thus streamlining product development and manufacturing.

First-in-Human (FIH) studies are designed and conducted based on nonclinical data submitted to and reviewed by regulatory authorities. For the very first trial in humans, nonclinical toxicology, pharmacology, pharmacokinetics, in vitro assays, etc. are conducted with the investigational compound and are used (among other purposes) to establish a “maximum recommended starting dose” (MRSD). There are two categories of approaches that can be used to help choose this dose: model-independent approaches and model-based approaches.

Determining the FIH dose for clinical studies is critical. Knowing how to select a safe starting dose and knowing what factors need to be taken into consideration is extremely important. Here we cover six key considerations related to determining the FIH dose for clinical studies.

• Accounting for uncertainty in the initial dose prediction

• Observing a weak dose-response relationship in the initial FIH study data

• Determining FIH dose for biologics compared to small molecules

• Validating model-based approaches for a FIH dose in a novel molecule

• Determining the best method if allometric scaling is not appropriate

• Incorporating multiple activity metrics into the FIH dose prediction

FIH dose prediction is a challenging endeavor that has inherent uncertainty. The best way to counteract this uncertainty is by choosing the best method for the compound of interest and, more often than not, this involves combining methods from both model-independent approaches and model-based approaches. Understanding the strength of each method as well as the particularities of the compound is essential to success in determining a safe and effective FIH dose in a Phase 1 clinical study.