Comparison of Tumor Binding Across Tumor Types and Cell Lines to Support Free Drug Considerations for Oncology Drug Discovery.

Tumor binding is an important parameter to derive unbound tumor concentration to explore pharmacokinetics (PK) and pharmacodynamics (PD) relationships for oncology disease targets. Tumor binding was evaluated using eleven matrices, including various commonly used ex vivo human and mouse xenograft and syngeneic tumors, tumor cell lines and liver as a surrogate tissue. The results showed that tumor binding is highly correlated among the different tumors and tumor cell lines except for the mouse melanoma (B16F10) tumor type. Liver fraction unbound (fu) has a good correlation with B16F10 tumor binding. Liver also demonstrates a two-fold equivalency, on average, with binding of other tumor types when a scaling factor is applied. Predictive models were developed for tumor binding, with correlations established with LogD (acids), predicted muscle fu (neutrals) and measured plasma protein binding (bases) to estimate tumor fu when experimental data are not available. Many approaches can be applied to obtain and estimate tumor binding values. One strategy proposed is to use a surrogate tumor tissue, such as mouse xenograft ovarian cancer (OVCAR3) tumor, as a surrogate for tumor binding (except for B16F10) to provide an early assessment of unbound tumor concentrations for development of PK/PD relationships.

The Comparison of Machine Learning and Mechanistic In Vitro-In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance.

Accurate prediction of human pharmacokinetics (PK) remains one of the key objectives of drug metabolism and PK (DMPK) scientists in drug discovery projects. This is typically performed by using in vitro-in vivo extrapolation (IVIVE) based on mechanistic PK models. In recent years, machine learning (ML), with its ability to harness patterns from previous outcomes to predict future events, has gained increased popularity in application to absorption, distribution, metabolism, and excretion (ADME) sciences. This study compares the performance of various ML and mechanistic models for the prediction of human IV clearance for a large (645) set of diverse compounds with literature human IV PK data, as well as measured relevant in vitro end points. ML models were built using multiple approaches for the descriptors: (1) calculated physical properties and structural descriptors based on chemical structure alone (classical QSAR/QSPR); (2) in vitro measured inputs only with no structure-based descriptors (ML IVIVE); and (3) in silico ML IVIVE using in silico model predictions for the in vitro inputs. For the mechanistic models, well-stirred and parallel-tube liver models were considered with and without the use of empirical scaling factors and with and without renal clearance. The best ML model for the prediction of in vivo human intrinsic clearance (CLint) was an in vitro ML IVIVE model using only six in vitro inputs with an average absolute fold error (AAFE) of 2.5. The best mechanistic model used the parallel-tube liver model, with empirical scaling factors resulting in an AAFE of 2.8. The corresponding mechanistic model with full in silico inputs achieved an AAFE of 3.3. These relative performances of the models were confirmed with the prediction of 16 Pfizer drug candidates that were not part of the original data set. Results show that ML IVIVE models are comparable to or superior to their best mechanistic counterparts. We also show that ML IVIVE models can be used to derive insights into factors for the improvement of mechanistic PK prediction.

Applying Two Orthogonal Methods to Assess Accuracy of Plasma Protein Binding Measurements for Highly Bound Compounds.

Significant advances have been made over the years to accurately measure plasma protein binding (PPB) of highly bound compounds. However, because of perceived uncertainty based on historical suboptimal methods and limitation of radiochemical purity of radiolabeled materials, current regulatory guidelines recommend using an arbitrary cutoff fraction unbound (fu) of 0.01 as the lower limit for drug-drug interaction (DDI) prediction. This can result in significant overprediction of DDI for highly bound compounds, unnecessary DDI clinical trials and more restrictive drug product labels. To build confidence in the accuracy of PPB measurement for highly bound compounds, 2 orthogonal methods, equilibrium dialysis and ultracentrifugation, are assessed in this study to measure PPB of 10 highly bound drugs (fu < 0.01). The results show that the 2 very different methods yield comparable fu values, generally within 2-fold of each other. The data suggest that PPB of highly bound compounds can be measured accurately using current state-of-art methods, and the experimental fu should be used for DDI prediction to provide a more realistic evaluation of DDI risk in the clinic.

Potential CRF1R PET imaging agents: 1-fluoroalkylsubstituted 5-halo-3-(arylamino)pyrazin-2(1H)-ones.

A series of pyrazinones were prepared and evaluated as potential CRF(1)R PET imaging agents. Optimization of their CRF(1)R binding potencies and octanol-phosphate buffer phase distribution coefficients are discussed herein.

[18F](R)-5-chloro-1-(1-cyclopropyl-2-methoxyethyl)-3-(4-(2-fluoroethoxy)-2,5-dimethyl phenylamino)pyrazin-2(1H)-one: introduction of N3-phenylpyrazinones as potential CRF-R1 PET imaging agents.

Based on a favorable balance between CRF-R1 affinity, lipophilicity and metabolic stability, compound 10 was evaluated for potential development as PET radioligand. Compound [(18)F]10 was prepared with high radiochemical purity and showed promising binding properties in rat brain imaging experiments.

Potential CRF1R PET imaging agents: N-fluoroalkyl-8-(6-methoxy-2-methylpyridin-3-yl)-2,7-dimethyl-N-alkylpyrazolo[1,5-a][1,3,5]triazin-4-amines.

A series of N-fluoroalkyl-8-(6-methoxy-2-methylpyridin-3-yl)-2,7-dimethyl-N-alkylpyrazolo[1,5-a][1,3,5]triazin-4-amines were prepared and evaluated as potential CRF(1)R PET imaging agents. Optimization of their CRF(1)R binding potencies and octanol-phosphate buffer phase distribution coefficients resulted in discovery of analog 7e (IC(50)=6.5 nM, logD=3.5).