Biomarkers and biomeasures: key enablers for pharmacokinetic-pharmacodynamic modeling in drug discovery and development

A pharmacokinetics-pharmacodynamics study of single-dose total glucosides of paeony capsule on reducing serum total bile acid in hepatic injury rats

CONTEXT

Total Glucosides of Paeony (TGP) capsule possesses various hepatoprotective activities. No study is available concerning TGP's concentration-effect relationship on hepatoprotection.

OBJECTIVE

To establish a pharmacokinetics-pharmacodynamics (PK-PD) modelling on TGP capsule's hepatoprotection after a single oral administration in hepatic injury rats.

MATERIALS AND METHODS

Male Sprague-Dawley rats were divided into five groups (n = 6): control, model (hepatic injury), treated-H (2.82 g/kg), treated-M (1.41 g/kg), and treated-L (0.705 g/kg) groups. All treated groups rats were intragastrically administered a single dose. An LC-MS/MS method was applied to determine paeoniflorin (Pae) and albiflorin (Alb) in rat serum. The effects of single-dose TGP on serum alanine transaminase (ALT), aspartate transaminase (AST) and total bile acid (TBA) were evaluated in hepatic injury rats.

RESULTS

Single dose (2.82, 1.41, or 0.705 g/kg) TGP capsule could real-time down-regulate serum TBA but not ALT and AST in hepatic injury rats within 20 h. An inhibitory effect Sigmoid E_max of PK-PD modelling was established using Pae and Alb as PK markers and serum TBA as effect index. Pharmacodynamic parameters were calculated. For treated-H, treated-M and treated-L group, respectively, E₀ were 158.1, 226.9 and 245.4 μmol/L for Pae, 146.1, 92.9 and 138.4 μmol/L for Alb, E_max were 53.0, 66.0, and 97.1 μmol/L for Pae, 117.4, 249.7 and 60.0 μmol/L for Alb, and EC₅₀ were 9.3, 5.2 and 2.7 μg/mL for Pae, 2.3, 0.8, and 0.8 μg/mL for Alb.

DISCUSSION AND CONCLUSIONS

Serum TBA is a sensitive effect index for TGP's single dose PK-PD modelling, and it is potential for further multi-dose studies of TGP' effect on hepatic injury. The study provides valuable information for TGP's mechanistic research and rational clinical application.

Challenges in translational drug research in neuropathic and inflammatory pain: the prerequisites for a new paradigm

AIM

Despite an improved understanding of the molecular mechanisms of nociception, existing analgesic drugs remain limited in terms of efficacy in chronic conditions, such as neuropathic pain. Here, we explore the underlying pathophysiological mechanisms of neuropathic and inflammatory pain and discuss the prerequisites and opportunities to reduce attrition and high-failure rate in the development of analgesic drugs.

METHODS

A literature search was performed on preclinical and clinical publications aimed at the evaluation of analgesic compounds using MESH terms in PubMed. Publications were selected, which focused on (1) disease mechanisms leading to chronic/neuropathic pain and (2) druggable targets which are currently under evaluation in drug development. Attention was also given to the role of biomarkers and pharmacokinetic-pharmacodynamic modelling.

RESULTS

Multiple mechanisms act concurrently to produce pain, which is a non-specific manifestation of underlying nociceptive pathways. Whereas these manifestations can be divided into neuropathic and inflammatory pain, it is now clear that inflammatory mechanisms are a common trigger for both types of pain. This has implications for drug development, as the assessment of drug effects in experimental models of neuropathic and chronic pain is driven by overt behavioural measures. By contrast, the use of mechanistic biomarkers in inflammatory pain has provided the pharmacological basis for dose selection and evaluation of non-steroidal anti-inflammatory drugs (NSAIDs).

CONCLUSION

A different paradigm is required for the identification of relevant targets and candidate molecules whereby pain is coupled to the cause of sensorial signal processing dysfunction rather than clinical symptoms. Biomarkers which enable the characterisation of drug binding and target activity are needed for a more robust dose rationale in early clinical development. Such an approach may be facilitated by quantitative clinical pharmacology and evolving technologies in brain imaging, allowing accurate assessment of target engagement, and prediction of treatment effects before embarking on large clinical trials.

Optimization of human dose prediction by using quantitative and translational pharmacology in drug discovery

In this perspective article, we explain how quantitative and translational pharmacology, when well-implemented, is believed to lead to improved clinical candidates and drug targets that are differentiated from current treatment options. Quantitative and translational pharmacology aims to build and continuously improve the quantitative relationship between drug exposure, target engagement, efficacy, safety and its interspecies relationship at every phase of drug discovery. Drug hunters should consider and apply these concepts to develop compounds with a higher probability of interrogating the clinical biological hypothesis. We offer different approaches to set an initial effective concentration or pharmacokinetic-pharmacodynamic target in man and to predict human pharmacokinetics that determine together the predicted human dose and dose schedule. All concepts are illustrated with ample literature examples.

Bench to bedside translation of antibody drug conjugates using a multiscale mechanistic PK/PD model: a case study with brentuximab-vedotin

To build a multiscale mechanism based pharmacokinetic-pharmacodynamic (PK/PD) model for antibody drug conjugates (ADCs), using brentuximab-vedotin as an example, for preclinical to clinical translation of ADC efficacy. Brentuximab-vedotin experimental data, collected from diverse publications, were employed in the following steps to build and validate the model: (1) characterization of ADC and payload PK at the cellular level, (2) characterization of payload PK in plasma and tumor tissue of xenograft mouse, (3) characterization of ADC PK in mouse plasma, (4) prediction of the tumor payload concentrations in xenograft mouse by integrating parameters obtained from steps 1-3 with the novel tumor disposition model for ADC, (5) characterization of preclinical brentuximab-vedotin tumor growth inhibition data using the novel PK/PD model, (6) characterization of ADC and payload PK in cancer patients, and (7) prediction of clinical responses of brentuximab-vedotin using the PK/PD model, by integrating PK parameters obtained from step 6, and translated mouse parameters from step 5; and comparing them with clinical trial results. The tumor disposition model was able to accurately predict xenograft tumor and plasma payload concentrations. PK/PD model predicted progression free survival rates and complete response rates for brentuximab-vedotin in patients were comparable to the observed clinical results. It is essential to understand and characterize the disposition of ADC and payload, at the cellular and physiological level, to predict the clinical outcome of ADC. A first of its kind mechanistic model has been developed for ADCs, which can integrate preclinical biomeasures and PK/PD data, to predict clinical response.