Applications of Model-Based Target Pharmacology Assessment in Defining Drug Design and DMPK Strategies: GSK Experiences.

Many critical decisions faced in early discovery require a thorough understanding of the dynamic behavior of pharmacological pathways following target engagement. From fundamental decisions on the optimal target to pursue and the ultimate drug product profile (combination of modality, potency, and compound properties) expected to elicit the desired clinical outcome to tactical program decisions such as what chemical series to pursue, what chemical properties require optimization, and what compounds to synthesize and progress, all demand detailed consideration of pharmacodynamics. Model-based target pharmacology assessment (mTPA) is a computational approach centered around large-scale virtual exploration of pharmacokinetic and pharmacodynamic models built early in discovery to guide these decisions. The present work summarizes several examples (use cases) from programs at GlaxoSmithKline that demonstrate the utility of mTPA throughout the drug discovery lifecycle.

Model-based Target Pharmacology Assessment (mTPA): An Approach Using PBPK/PD Modeling and Machine Learning to Design Medicinal Chemistry and DMPK Strategies in Early Drug Discovery.

The optimal pharmacokinetic (PK) required for a drug candidate to elicit efficacy is highly dependent on the targeted pharmacology, a relationship that is often not well characterized during early phases of drug discovery. Generic assumptions around PK and potency risk misguiding screening and compound design toward nonoptimal absorption, distribution, metabolism, and excretion (ADME) or molecular properties and ultimately may increase attrition as well as hit-to-lead and lead optimization timelines. The present work introduces model-based target pharmacology assessment (mTPA), a computational approach combining physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling, sensitivity analysis, and machine learning (ML) to elucidate the optimal combination of PK, potency, and ADME specific for the targeted pharmacology. Examples using frequently encountered PK/PD relationships are presented to illustrate its application, and the utility and benefits of deploying such an approach to guide early discovery efforts are discussed.

Investigation of the Sensitivity of Transmission Raman Spectroscopy for Polymorph Detection in Pharmaceutical Tablets.

Polymorph detection is critical for ensuring pharmaceutical product quality in drug substances exhibiting polymorphism. Conventional analytical techniques such as X-ray powder diffraction and solid-state nuclear magnetic resonance are utilized primarily for characterizing the presence and identity of specific polymorphs in a sample. These techniques have encountered challenges in analyzing the constitution of polymorphs in the presence of other components commonly found in pharmaceutical dosage forms. Laborious sample preparation procedures are usually required to achieve satisfactory data interpretability. There is a need for alternative techniques capable of probing pharmaceutical dosage forms rapidly and nondestructively, which is dictated by the practical requirements of applications such as quality monitoring on production lines or when quantifying product shelf lifetime. The sensitivity of transmission Raman spectroscopy for detecting polymorphs in final tablet cores was investigated in this work. Carbamazepine was chosen as a model drug, polymorph form III is the commercial form, whereas form I is an undesired polymorph that requires effective detection. The concentration of form I in a direct compression tablet formulation containing 20% w/w of carbamazepine, 74.00% w/w of fillers (mannitol and microcrystalline cellulose), and 6% w/w of croscarmellose sodium, silicon dioxide, and magnesium stearate was estimated using transmission Raman spectroscopy. Quantitative models were generated and optimized using multivariate regression and data preprocessing. Prediction uncertainty was estimated for each validation sample by accounting for all the main variables contributing to the prediction. Multivariate detection limits were calculated based on statistical hypothesis testing. The transmission Raman spectroscopic model had an absolute prediction error of 0.241% w/w for the independent validation set. The method detection limit was estimated at 1.31% w/w. The results demonstrated that transmission Raman spectroscopy is a sensitive tool for polymorphs detection in pharmaceutical tablets.

Designed blending for near infrared calibration.

Spectroscopic methods are increasingly used for monitoring pharmaceutical manufacturing unit operations that involve powder handling and processing. With that regard, chemometric models are required to interpret the obtained spectra. There are many ways to prepare artificial powder blend samples used in a chemometric model for predicting the chemical content. Basically, an infinite number of possible concentration levels exist in terms of the individual components. In our study, design of experiments for ternary mixtures was used to establish a suitable number of blend compositions that represents the entire mixture region of interest for a three component blend. Various experimental designs and their effect on the predictive power of a chemometric model for near infrared spectra were investigated. It was determined that a particular choice of experimental design could change the predictive power of a model, even with the same number of calibration experiments.

Effect of experimental design on the prediction performance of calibration models based on near-infrared spectroscopy for pharmaceutical applications.

Near-infrared spectroscopy (NIRS) is a valuable tool in the pharmaceutical industry, presenting opportunities for online analyses to achieve real-time assessment of intermediates and finished dosage forms. The purpose of this work was to investigate the effect of experimental designs on prediction performance of quantitative models based on NIRS using a five-component formulation as a model system. The following experimental designs were evaluated: five-level, full factorial (5-L FF); three-level, full factorial (3-L FF); central composite; I-optimal; and D-optimal. The factors for all designs were acetaminophen content and the ratio of microcrystalline cellulose to lactose monohydrate. Other constituents included croscarmellose sodium and magnesium stearate (content remained constant). Partial least squares-based models were generated using data from individual experimental designs that related acetaminophen content to spectral data. The effect of each experimental design was evaluated by determining the statistical significance of the difference in bias and standard error of the prediction for that model's prediction performance. The calibration model derived from the I-optimal design had similar prediction performance as did the model derived from the 5-L FF design, despite containing 16 fewer design points. It also outperformed all other models estimated from designs with similar or fewer numbers of samples. This suggested that experimental-design selection for calibration-model development is critical, and optimum performance can be achieved with efficient experimental designs (i.e., optimal designs).