PBK Human

What is PBK modeling and how is it used in human safety assessment?

PBK modeling is a computational approach that uses mathematical equations to simulate the absorption, distribution, metabolism, and excretion (ADME) of compounds in humans. These models integrate physiological parameters with chemical-specific data to predict internal exposure levels and potential toxic effects.

In human safety assessment, PBK modeling serves as a crucial component of Next Generation Risk Assessment (NGRA) frameworks. When Threshold of Toxicological Concern (TTC) approaches are insufficient to ensure safety, PBK models offer a more refined estimation of systemic exposure. They translate external exposure (applied dose) to internal exposure metrics such as maximum blood concentration (Cmax) or area under the curve (AUC), which can then be compared to in vitro points of departure to derive bioactivity exposure ratios (BERs) .

The implementation of PBK modeling in safety assessment typically follows a tiered approach:

• Level 0: Characterization of exposure scenarios and collection of existing data

• Level 1: Predictions using in silico parameterization only

• Level 2: PBK modeling based on in vitro parameterization

• Level 3: Generation of human pharmacokinetic data for validation and calibration

This structured approach allows risk assessors to employ tools of appropriate complexity for decision-making, progressing to more sophisticated methods only when necessary.

What types of input data are required for PBK model parameterization?

PBK models require three categories of input data for accurate parameterization:

• Physiological parameters:

  • Organ volumes

  • Blood flow rates

  • Tissue composition (water, lipid, protein content)

  • Demographic factors (age, sex, weight)

• Anatomical and physiological data:

  • Cardiac output

  • Ventilation rates (for inhalation exposure)

  • Gastrointestinal transit times (for oral exposure)

  • Skin properties (for dermal exposure)

• Chemical-specific parameters:

  • Physicochemical properties (molecular weight, logP, pKa)

  • Absorption parameters (permeability, dissolution)

  • Distribution parameters (tissue:plasma partition coefficients)

  • Metabolism parameters (intrinsic clearance)

  • Excretion parameters (renal clearance)

These parameters can be obtained through various approaches including in vitro experimental measurements, in silico prediction methods, and literature values. The selection of appropriate parameterization methods significantly influences model predictive performance, as demonstrated in studies comparing different approaches for calculating tissue:plasma partition coefficients and fraction unbound in plasma .

How does PBK modeling accuracy compare with in vivo human data?

The accuracy of PBK models depends largely on the quality of input parameters and the structural appropriateness of the model for the specific compound being studied. Recent comprehensive evaluations provide insights into typical performance metrics:

A systematic study evaluated 38,772 Cmax predictions for 44 compounds using different combinations of in vitro and in silico parameterization approaches. The best performing model configuration achieved:

• 19 out of 44 compounds (43%) predicted within 2-fold of observed Cmax

• 34 out of 44 compounds (77%) predicted within 5-fold of observed Cmax

• 10 compounds (23%) were overestimated by more than 5-fold

• No compounds were underestimated by more than 5-fold

Best results were achieved when the hepatic clearance was parameterized based on in vitro (i.e., hepatocytes or liver S9) measured intrinsic clearance values, combined with the method of Rodgers and Rowland for calculating tissue:plasma partition coefficients, and the method of Lobell and Sivarajah for calculating the fraction unbound in plasma .

These findings indicate that while PBK models can provide reasonably accurate predictions for the majority of compounds, there remains a significant subset for which predictions deviate substantially from observed values.

What methodological approaches can optimize PBK model performance?

Several methodological approaches can be employed to optimize PBK model performance:

• Systematic parameter selection:

  • Use experimental data for the most sensitive parameters

  • Apply appropriate in silico methods for physicochemical properties

  • Incorporate uncertainty analysis for less influential parameters

• Tiered implementation strategy:

  • Begin with simple models and increase complexity as needed

  • Progress from Level 1 (in silico only) to Level 2 (in vitro parameterization) based on uncertainty analysis

  • Add chemical-specific mechanisms when generic models show limitations

• Validation approaches:

  • Compare predictions against in vivo human data when available

  • Use cross-chemical validation within similar chemical spaces

  • Evaluate consistency across multiple parameterization methods

• Structural considerations:

  • Add additional compartments for compounds with unique distribution patterns

  • Include specialized processes (enterohepatic circulation, active transport) for relevant compounds

  • Consider physiological factors affecting compound-specific kinetics

The framework shown in the search results emphasizes an iterative approach where sensitivity analysis identifies the most influential parameters, which then receive the most rigorous experimental or computational attention .

How can sensitivity analysis improve PBK model reliability?

Sensitivity analysis provides a systematic approach to quantify how variation in model inputs affects the outputs, offering crucial insights for model refinement. The methodology involves:

• Local sensitivity analysis:

  • Vary one parameter at a time by a small percentage (e.g., ±10%)

  • Calculate sensitivity coefficients (ratio of % change in output to % change in input)

  • Rank parameters by their influence on model predictions

  • Identify parameters requiring more accurate determination

• Global sensitivity analysis:

  • Simultaneously vary multiple parameters across their plausible ranges

  • Use methods such as Monte Carlo simulations, Sobol indices, or Morris screening

  • Assess parameter interactions and non-linear effects

  • Generate probability distributions of model outputs

According to the framework described in the search results, sensitivity analysis is a critical step after initial parameterization to determine the influential parameters that require more accurate determination through in vitro experiments .

This approach ensures that the most critical parameters are characterized with appropriate precision, while less influential parameters can be estimated using simpler methods, optimizing both model performance and resource utilization.

What are the best approaches for parameterizing hepatic clearance in human PBK models?

Hepatic clearance is often the most influential parameter in PBK models, and its accurate determination is critical for reliable predictions. Based on systematic comparisons, the following methodological approaches show superior performance:

• In vitro measurement systems:

  • Primary human hepatocytes (preferred for compounds with phase II metabolism)

  • Human liver microsomes (suitable for CYP-mediated metabolism)

  • Liver S9 fractions (offering a balance of phase I and II enzymes)

• Extrapolation methods:

  • Well-stirred model for scaling in vitro intrinsic clearance to whole liver clearance

  • Appropriate correction for binding in the in vitro system and in plasma

  • Scaling factors based on enzyme content per gram of liver and liver weight

• Alternative systems for challenging compounds:

  • Long-term hepatocyte cultures for slowly metabolized compounds

  • Hepatocyte co-cultures maintaining metabolic capacity

  • Microfluidic liver-on-chip systems for improved physiological relevance

The search results specifically indicate that models using in vitro measured intrinsic clearance values from hepatocytes or liver S9 fractions demonstrated superior predictive performance compared to those relying solely on in silico predictions .

How are tissue:plasma partition coefficients best calculated for accurate PBK predictions?

Tissue:plasma partition coefficients (Kp values) determine compound distribution in the body and significantly impact PBK model predictions. Based on systematic evaluations, the following methodological approaches provide optimal accuracy:

• Selection of calculation methods based on compound properties:

• Required input parameters for calculation:

  • Compound-specific: Lipophilicity (logP), pKa values, fraction unbound in plasma

  • Tissue-specific: Water content, neutral lipid content, phospholipid content, pH

According to the search results, the method of Rodgers and Rowland for calculating tissue:plasma partition coefficients contributed to more accurate Cmax predictions when combined with appropriate clearance and plasma protein binding parameterization .

How can PBK models be validated without human in vivo data?

Validating PBK models without human in vivo data represents a significant challenge but is increasingly necessary in the shift toward non-animal approaches. Several methodological strategies have emerged:

• Cross-chemical validation:

  • Select a training set of compounds with known human in vivo data

  • Establish the predictive performance of the model for these compounds

  • Identify chemical space boundaries where the model performs well

  • Apply the model only to new compounds within this chemical space

• In vitro to in vitro extrapolation:

  • Conduct in vitro experiments simulating key ADME processes

  • Compare PBK model predictions to these simplified systems

  • Validate components of the model individually

• Weight-of-evidence approach:

  • Use multiple parameterization methods and model structures

  • Compare predictions across different approaches

  • Assess consistency of predictions as a measure of reliability

  • Apply uncertainty factors based on consistency analysis

These approaches align with the framework described in the search results, where progression to higher levels of complexity (including human PK data generation) occurs only when necessary for decision-making .

What is PDZ Binding Kinase (PBK) and what is its role in human cells?

PDZ Binding Kinase (PBK), also known as T-lymphokine-activated killer cell-originated protein kinase (TOPK), is a serine-threonine kinase that belongs to the mitogen-activated protein kinase kinase (MAPKK) family. In normal human cells, PBK plays several critical roles:

• Cell cycle regulation:

  • Phosphorylates mitotic proteins during the mitotic phase

  • Participates in sister chromatid segregation

  • Contributes to mitotic checkpoint regulation

• DNA damage response:

  • Involved in the DNA integrity checkpoint

  • Participates in DNA-dependent DNA replication processes

  • Responds to cellular stress through pathway activation

• Normal tissue expression:

  • Minimally expressed in most normal adult tissues

  • Primarily active in highly proliferative tissues (e.g., testis, fetal tissues)

  • Transiently expressed during immune cell activation

The search results indicate that PBK "is barely expressed in normal tissues" under physiological conditions, suggesting tight regulation of its expression and activation .

How is PBK expression regulated in normal tissues versus cancer tissues?

PBK expression shows marked differences between normal and cancer tissues, with complex regulatory mechanisms controlling its expression:

In normal tissues:

• Tightly controlled expression, predominantly in testis and fetal tissues

• Minimal to undetectable expression in most differentiated adult tissues

• Transient expression during specific cellular processes (e.g., immune cell activation)

• Regulation by cell cycle-dependent transcription factors

In cancer tissues:

• Significantly upregulated in most cancer types compared to normal counterparts

• Expression correlates with proliferation markers

• Associated with tumor stage and progression

• Regulated by oncogenic signaling pathways

The search results specifically note that "PBK expression is relatively high in most cancers compared to their normal counterparts, and this gene is barely expressed in normal tissues."

Data supporting these observations were obtained through comprehensive analysis of RNA-seq data from TCGA and GTEx databases across 33 cancer types and corresponding normal tissues, providing a systematic comparison of expression patterns .

What is the relationship between PBK expression and tumor stages?

PBK expression shows distinct patterns across different tumor stages, providing insights into its role in cancer progression:

• Stage-specific expression:

  • Early stages (I-II): PBK expression is already elevated compared to normal tissue

  • Intermediate stages (II-III): Further increased expression in many cancer types

  • Advanced stages (III-IV): Expression patterns become more complex

The search results specifically state: "PBK expression was correlated with the tumor stage in various cancers. Especially between stage I, II, and III tumors, PBK expression is significantly different."

• Cancer-specific patterns:

  • Most cancers: Progressive increase in expression with advancing stage

  • COAD (Colon adenocarcinoma): Expression peaks at stage II, then decreases in advanced stages

  • LUSC (Lung squamous cell carcinoma): Similar pattern with downregulation in advanced stages

The search results highlight this unusual pattern: "Interestingly, we found that the PBK expression in COAD and LUSC is downregulated in the advanced stage." This finding suggests cancer-specific roles for PBK at different stages of progression.

• Clinical implications:

  • Early diagnostic potential: Elevated even in stage I tumors

  • Monitoring: Changes during treatment and progression

  • Prognostic: Stage-specific relationships with outcomes

  • Therapeutic: Targeting may be most effective at specific stages

These findings suggest that while PBK overexpression is generally associated with cancer progression, the relationship is not always linear and varies by cancer type.

How does PBK expression correlate with patient prognosis in different cancer types?

PBK expression shows significant associations with patient outcomes across multiple cancer types, making it a potential prognostic biomarker:

These findings indicate that PBK expression has significant prognostic value across multiple cancer types, predominantly associated with poorer outcomes, with the potential to enhance current prognostic models in clinical practice.

How does PBK expression correlate with immune infiltration in different cancer types?

PBK expression shows complex relationships with immune infiltration in the tumor microenvironment, with distinct patterns across cancer types and immune cell populations:

These findings suggest that PBK may influence tumor progression partly through modulation of the immune microenvironment, with implications for combined targeting strategies and immunotherapy response prediction.

What molecular pathways are associated with PBK overexpression in cancer?

PBK overexpression in cancer is associated with specific molecular pathways that contribute to tumor progression. GSEA analysis revealed several key pathways:

• Cell cycle and mitosis pathways:

  • Mitotic nuclear division and sister chromatid segregation (in ACC)

  • DNA-dependent DNA replication, DNA integrity checkpoint, and mitotic cell cycle checkpoint (in LUAD)

  • Cell cycle and DNA replication (in BLCA, LUAD, and UCEC)

• Sensory perception pathways:

  • Detection of chemical stimulus (negatively correlated in BLCA, PRAD, and UCEC; positively in HNSC and LUAD)

  • Olfactory transduction (enriched in BLCA, HNSC, PRAD, and UCEC)

• Cancer-specific signaling pathways:

  • Hippo-YAP signaling in breast cancer: "PBK also elevates in breast cancer and serves as a downstream target of Hippo-YAP signaling"

  • ETV4-uPAR signaling pathway in hepatocellular carcinoma: "PBK promotes invasion and migration via the ETV4-uPAR signaling pathway in hepatocellular carcinoma"

The search results emphasize that "GSEA analysis revealed that PBK participated in a wide range of functions and pathways relevant to the cell cycle and DNA replication... All these terms were enriched in the PBK high-expression side, which suggested that high PBK expression mainly involved these signaling pathways, participated in mitosis and the cell cycle, and may also function in promoting tumor cell proliferation."

These findings collectively suggest that PBK primarily functions in promoting cell cycle progression, DNA replication, and mitosis in cancer cells, consistent with its role as an oncogenic kinase driving proliferation across multiple cancer types.

What is the relationship between PBK and tumor mutation burden (TMB) or microsatellite instability (MSI)?

PBK expression shows significant associations with genomic instability metrics including tumor mutation burden (TMB) and microsatellite instability (MSI) across multiple cancer types:

• Association with Tumor Mutation Burden (TMB):

  • Strong positive correlation in 23 out of 33 cancer types

  • "PBK expression is strongly associated with TMB in 23 cancer types"

• Association with Microsatellite Instability (MSI):

  • Significant correlation in 9 out of 33 cancer types

  • "PBK expression is associated with MSI in nine cancer types"

• Potential biological mechanisms:

  • DNA damage response connection: PBK involvement in DNA integrity checkpoint

  • Cell cycle checkpoint regulation: Mitotic checkpoint functions

  • Genomic instability resulting from checkpoint dysregulation

These associations between PBK expression and genomic instability markers may have important implications for predicting response to immunotherapy, as high TMB and MSI are established biomarkers for checkpoint inhibitor efficacy in multiple cancer types.

How does PBK expression affect drug response and resistance in cancer?

PBK expression has emerging roles in drug response and resistance across different cancer types and drug classes:

• Positive correlations with drug effectiveness:

  • "PBK is positively correlated with the effectiveness of some chemotherapeutics such as trametinib, RDEA119, PD032590, and selumetinib"

  • These are primarily MEK inhibitors, suggesting pathway-specific interactions

• Drug resistance associations:

  • "High PBK expression was correlated with a poor prognosis, metastasis, and cisplatin resistance in high-grade serous ovarian carcinoma (HGSOC)"

  • This indicates a role in platinum resistance in ovarian cancer

• Potential for therapeutic targeting:

  • "Targeting PBK in ACC cell lines decreased the function of cell proliferation, clonogenicity, and anchorage-independent growth"

  • "The employment of a PBK/TOPK inhibitor could inhibit the proliferation of lung cancer cells"

• Mechanistic contributions to resistance:

  • Cell cycle regulation effects: Altered checkpoint responses affecting drug sensitivity

  • DNA damage response modulation: Enhanced repair capacity after genotoxic stress

  • Anti-apoptotic signaling: Activation of survival pathways

These findings suggest that PBK modulation could be a valuable strategy for chemosensitization and overcoming resistance, particularly for specific drug classes like MEK inhibitors and platinum compounds in relevant cancer types.