Current Clinical Pharmacology - Volume 17, Issue 1, 2022

The use and acceptance of cannabis, either medically or recreationally, has substantially outpaced the collection of data necessary to evaluate it’s use in any population. However, the mere widespread availability does not imply the absence of risk or confirmation of efficacy and should not be treated as such. There is enough data to suggest that not only does the potential for pharmacokinetic and metabolic interactions exist, but also that baseline characteristics for a given population could be different in chronic cannabis users. Either or both of these may impact the safety and efficacy profile for any new drug in development. As such, we encourage drug developers to consider that the cannabis user may very well be a special population that warrants its own clinical pharmacology evaluation.

Background: It has been recognized that patients should be involved in the design of clinical trials. However, there is a lack of agreement on what patient-centricity means. Methods: In this article, a Patient Motivation Pyramid based on Maslow’s theory of human motivation is introduced as a tool to identify patient needs. This pyramid is used to make a comprehensive overview of options to implement a patient-centric trial design. The Pyramid with the described options can help to identify patient-centric activities suitable for given drug development. The current article further describes the potential benefits of patient-centric trial designs with an emphasis on early clinical development. Especially in early clinical development, during which trials have many assessments per patient, and the safety and clinical efficacy are uncertain, patient-centric trial design can improve feasibility. Finally, we present three case examples on patient-centric trial design. The first example is seeking patient input on the trial design for a First-in-Human trial which includes patients with Immune Thrombocytopenic Purpura. The second example is the use of a video-link for home dosing. The final example is the use of digital medicine in a decentralized trial in heart failure patients. Results: A comprehensive overview of patients’ needs can be accomplished by building a Patient Motivation Pyramid as a tool. Patient input can lead to improved endpoints, improved feasibility, better recruitment, less dropout, less protocol amendments, and higher patient satisfaction. The use of digital medicine can lead to a trial design with much less visits to the clinical research center in early clinical development and in a later development phase, even to a complete virtual trial. Conclusion: We recommend using the Patient Motivation Pyramid as a structural approach for identifying elements of patient-centricity. Secondly, we recommend starting using patient-centric approaches in an early phase of the medicine’s lifecycle.

Cannabis has become legal in much of the United States similar to many other countries, for either recreational or medical use. The use of cannabis products is rapidly increasing while the body of knowledge of its myriad of effects still lags. In vitro and clinical data show that cannabis’ main constituents, delta-9-tetrahydrocannabinol and cannabidiol, can affect pharmacokinetics (PK), safety, and pharmacodynamics (PD) of other drugs. Within the context of clinical drug development, the widespread and frequent use of cannabis products has essentially created another special population: the cannabis user. We propose that all clinical drug development programs include a Phase 1 study to assess the drug-drug interaction potential of cannabis as a precipitant on the PK, safety, and if applicable, the PD of all new molecular entities (NMEs) in a combination of healthy adult subjects as well as frequent and infrequent cannabis users. This data should be required to inform drug labeling and aid health care providers in treating any patient, as cannabis has quickly become another common concomitant medication and cannabis users, a new special population.

Background: We assessed the extent to which urinary and fecal excretion of 14C-labeled drug material in animal ADME studies was predictive of human ADME studies. We compared observed plasma elimination half-lives for total drug-related radioactivity in humans to pre-study predictions, and we estimated the impact of any major differences on human dosimetry calculations. Methods: We included 34 human ADME studies with doses of 14C above 0.1 MBq. We calculated ratios of dosimetry input parameters (percentage fecal excretion in humans versus animals; observed half-life in humans versus predicted pre-study) and output parameters (effective dose post-study versus pre-study) and assessed their relationship. Results: A quantitative correlation assessment did not show a statistically significant correlation between the ratios of percentages of 14C excreted in feces and the ratios of dosimetry outcomes in the entire dataset, but a statistically significant correlation was found when assessing the studies that were based on ICRP 60/62 (n=19 studies; P=0.0028). There also appeared to be a correlation between the plasma half-life ratios and the ratios of dosimetry results. A quantitative correlation assessment showed that there was a statistically significant correlation between these ratios (P<0.0001). Conclusion: In all cases where the plasma elimination half-life for 14C in humans was found to be longer than the predicted value, the radiation burden was still within ICRP Category IIa. Containment of the actual radiation burden below the limit of 1.00 mSv appeared to be determined partly also by our choice to limit 14C doses to 3.7 MBq.

Characterized by small, highly heterogeneous patient populations, rare disease trials magnify the challenges often encountered in traditional clinical trials. In recent years, there have been increased efforts by stakeholders to improve drug development in rare diseases through novel approaches to clinical trial designs and statistical analyses. We highlight and discuss some of the current and emerging approaches aimed at overcoming challenges in rare disease clinical trials, with a focus on the ultimate stakeholder, the patient.

Background: Over the last decades, many brain imaging studies have contributed to new insights in the pathogenesis of psychiatric disease. However, in spite of these developments, progress in the development of novel therapeutic drugs for prevalent psychiatric health conditions has been limited. Objective: In this review, we discuss translational, diagnostic and methodological issues that have hampered drug development in CNS disorders with a particular focus on psychiatry. The role of preclinical models is critically reviewed and opportunities for brain imaging in early stages of drug development using PET and fMRI are discussed. The role of PET and fMRI in drug development is reviewed emphasizing the need to engage in collaborations between industry, academia and phase I units. Results: Brain imaging technology has revolutionized the study of psychiatric illnesses, and during the last decade, neuroimaging has provided valuable insights at different levels of analysis and brain organization, such as effective connectivity (anatomical), functional connectivity patterns and neurochemical information that may support both preclinical and clinical drug development. Conclusion: Since there is no unifying pathophysiological theory of individual psychiatric syndromes and since many symptoms cut across diagnostic boundaries, a new theoretical framework has been proposed that may help in defining new targets for treatment and thus enhance drug development in CNS diseases. In addition, it is argued that new proposals for data-mining and mathematical modelling as well as freely available databanks for neural network and neurochemical models of rodents combined with revised psychiatric classification will lead to new validated targets for drug development.

Background: Opioid analgesics used to treat pain can cause respiratory depression. However, this effect has not been extensively studied, and life-threatening, opioid-induced respiratory depression remains difficult to predict. We tested the ventilatory response to hypercapnia for evaluating the pharmacodynamic effect of a drug on respiratory depression. Methods: We conducted a randomized, placebo-controlled, double-blind, crossover study on 12 healthy adult males. Subjects received 2 treatments (placebo and immediate-release oxycodone 30 mg) separated by a 24-hour washout period. Subjects inhaled a mixture of 7% carbon dioxide, 21% oxygen, and 72% nitrogen for 5 minutes to assess respiratory depression. Minute ventilation, respiratory rate, tidal volume, flow rate, end-tidal CO2, and oxygen saturation were recorded continuously at pre-dose and 30, 60, 120, and 180 minutes post-dose. The primary endpoint was the effect on the ventilatory response to hypercapnia at 60 minutes post-dose, as assessed by the slope of the linear relationship between minute ventilation and end-tidal CO2. Results: At 60 minutes post-dose, subjects had a mean slope of 2.4 in the oxycodone crossover period, compared to 0.1 in the placebo period (mean difference, 2.3; 95% CI: 0.2 to 4.5; p = 0.035). Statistical significance was likewise achieved at the secondary time points (30, 120, and 180 minutes post-dose, p <0.05). Conclusions: This model for testing ventilatory response to hypercapnia discriminated the effect of 30 mg of oxycodone vs. placebo for up to 3 hours after a single dose. It may serve as a method to predict the relative effect of a drug on respiratory depression.