PF-06651600, a newly discovered potent JAK3-selective inhibitor, is highly efficacious at inhibiting γc cytokine signaling, which is dependent on both JAK1 and JAK3. PF-06651600 allowed the comparison of JAK3-selective inhibition to pan-JAK or JAK1-selective inhibition, in relevant immune cells to a level that could not be achieved previously without such potency and selectivity. In vitro, PF-06651600 inhibits Th1 and Th17 cell differentiation and function, and in vivo it reduces disease pathology in rat adjuvant-induced arthritis as well as in mouse experimental autoimmune encephalomyelitis models. Importantly, by sparing JAK1 function, PF-06651600 selectively targets γc cytokine pathways while preserving JAK1-dependent anti-inflammatory signaling such as the IL-10 suppressive functions following LPS treatment in macrophages and the suppression of TNFα and IL-1β production in IL-27-primed macrophages. Thus, JAK3-selective inhibition differentiates from pan-JAK or JAK1 inhibition in various immune cellular responses, which could potentially translate to advantageous clinical outcomes in inflammatory and autoimmune diseases.

Severe drug-induced liver injury (DILI) remains a major safety issue due to its frequency of occurrence, idiosyncratic nature, poor prognosis, and diverse underlying mechanisms. Numerous experimental approaches have been published to improve human DILI prediction with modest success. A retrospective analysis of 125 drugs (70 = most-DILI, 55 = no-DILI) from the Food and Drug Administration Liver Toxicity Knowledge Base was used to investigate DILI prediction based on consideration of human exposure alone or in combination with mechanistic assays of hepatotoxic liabilities (cytotoxicity, bile salt export pump inhibition, or mitochondrial inhibition/uncoupling). Using this dataset, human plasma Cmax,total ≥ 1.1 µM alone distinguished most-DILI from no-DILI compounds with high sensitivity/specificity (80/73%). Accounting for human exposure improved the sensitivity/specificity for each assay and helped to derive predictive safety margins. Compounds with plasma Cmax,total ≥ 1.1 µM and triple liabilities had significantly higher odds ratio for DILI than those with single/dual liabilities. Using this approach, a subset of recent pharmaceuticals with evidence of liver injury during clinical development was recognized as potential hepatotoxicants. In summary, plasma Cmax,total ≥ 1.1 µM along with multiple mechanistic liabilities is a major driver for predictions of human DILI potential. In applying this approach during drug development the challenge will be generating accurate estimates of plasma Cmax,total at efficacious doses in advance of generating true exposure data from clinical studies. In the meantime, drug candidates with multiple hepatotoxic liabilities should be deprioritized, since they have the highest likelihood of causing DILI in case their efficacious plasma Cmax,total in humans is higher than anticipated.

(Z)-6-Hydroxynorketamine (3), a secondary metabolite of the dissociative anesthetic agent ketamine (1), was synthesized, and its central nervous system (CNS) properties were compared to those of the parent drug and the primary metabolite, norketamine (2). Administration of compounds 1 and 2 to rats (40 mg/kg iv) produced general anesthesia and also led to marked increases in spontaneous locomotor activity during the postanesthetic recovery phase. These effects were of significantly longer duration with 1 than with 2. In contrast, the same dose of 3 produced neither general anesthesia nor CNS excitation, despite the fact that 3 entered brain tissue readily from the systemic circulation. It is concluded that the CNS effects of 1 are attenuated by metabolism to 2 and are abolished by subsequent hydroxylation to produce 3. Moreover, the results suggest that the desirable anesthetic properties of 1 and related arylcyclohexylamines may be inseparable from their ability to produce adverse postanesthetic emergence reactions.

Protein tyrosine phosphatase 1B (PTP1B) is a negative regulator of the insulin and leptin receptor pathways and thus an attractive therapeutic target for diabetes and obesity. Starting with a high micromolar lead compound, structure-based optimization of novel PTP1B inhibitors by extension of the molecule from the enzyme active site into the second phosphotyrosine binding site is described. Medicinal chemistry, guided by X-ray complex structure and molecular modeling, has yielded low nanomolar PTP1B inhibitors in an efficient manner. Compounds from this chemical series were found to be actively transported into hepatocytes. This active uptake into target tissues could be one of the possible avenues to overcome the poor membrane permeability of PTP1B inhibitors.

Significant work has been dedicated to the discovery of JAK kinase inhibitors resulting in several compounds entering clinical development and two FDA approved NMEs. However, despite significant effort during the past 2 decades, identification of highly selective JAK3 inhibitors has eluded the scientific community. A significant effort within our research organization has resulted in the identification of the first orally active JAK3 specific inhibitor, which achieves JAK isoform specificity through covalent interaction with a unique JAK3 residue Cys-909. The relatively rapid resynthesis rate of the JAK3 enzyme presented a unique challenge in the design of covalent inhibitors with appropriate pharmacodynamics properties coupled with limited unwanted off-target reactivity. This effort resulted in the identification of 11 (PF-06651600), a potent and low clearance compound with demonstrated in vivo efficacy. The favorable efficacy and safety profile of this JAK3-specific inhibitor 11 led to its evaluation in several human clinical studies.

It is generally believed that metabolic bioactivation of drug molecules to form reactive metabolites, followed by their covalent binding to endogenous macromolecules, is one of the mechanisms that can lead to hepatotoxicity or idiosyncratic adverse drug reactions (IADRs). Although the role of bioactivation in drug-induced liver injury has been reasonably well established and accepted, and methodologies (e.g., structural alerts, reactive metabolite trapping, and covalent binding) continue to emerge in an attempt to detect the occurrence of bioactivation, the challenge remains to accurately predict the likelihood for idiosyncratic liver toxicity. Recent advances in risk-assessment methodologies, such as by the estimate of total body burden of covalent binding or by zone classification, taking the clinical dose into consideration, are positive steps toward improving risk assessment. The ability to better predict the potential of a drug candidate to cause IADRs will further be dependent upon a better understanding of the pathophysiological mechanisms of such reactions. Until a thorough understanding of the relationship between liver toxicity and the formation of reactive metabolites is achieved, it appears, at present, that the most practical strategy in drug discovery and development to reduce the likelihood of idiosyncratic liver toxicity via metabolic activation is to minimize or eliminate the occurrence of bioactivation and, at the same time, to maximize the pharmacological potency (to minimze the clinical dose) of the drug of interest.

Under the guidance of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ), scientists from 20 pharmaceutical companies formed a Victim Drug-Drug Interactions Working Group. This working group has conducted a review of the literature and the practices of each company on the approaches to clearance pathway identification (f CL ), estimation of fractional contribution of metabolizing enzyme toward metabolism (f m ), along with modeling and simulation-aided strategy in predicting the victim drug-drug interaction (DDI) liability due to modulation of drug metabolizing enzymes. Presented in this perspective are the recommendations from this working group on: 1) strategic and experimental approaches to identify f CL and f m , 2) whether those assessments may be quantitative for certain enzymes (e.g., cytochrome P450, P450, and limited uridine diphosphoglucuronosyltransferase, UGT enzymes) or qualitative (for most of other drug metabolism enzymes), and the impact due to the lack of quantitative information on the latter. Multiple decision trees are presented with stepwise approaches to identify specific enzymes that are involved in the metabolism of a given drug and to aid the prediction and risk assessment of drug as a victim in DDI. Modeling and simulation approaches are also discussed to better predict DDI risk in humans. Variability and parameter sensitivity analysis were emphasized when applying modeling and simulation to capture the differences within the population used and to characterize the parameters that have the most influence on the prediction outcome.

The hepatic risk matrix (HRM) was developed and used to differentiate lead clinical and back-up drug candidates against competitor/ marketed drugs within the same pharmaceutical class for their potential to cause human drug-induced liver injury (DILI). The hybrid HRM scoring system blends physicochemical properties (Rule of Two Model: dose and lipophilicity or Partition Model: dose, ionization state, lipophilicity, and fractional carbon bond saturation) with common toxicity mechanisms (cytotoxicity, mitochondrial dysfunction, and bile salt export pump (BSEP) inhibition) that promote DILI. HRM scores are based on bracketed safety margins (<1, 1−10, 10−100, and >100× clinical C max,total ). On the basis of well-established clinical safety experience of marketed/withdrawn drug candidates, the background analysis consists of 200 drugs from the Liver Toxicity Knowledge Base annotated as Most-DILI-(79), Less-DILI-(56), No-DILI-(47), and Ambiguous-DILI-concern (18) drugs. Scores were generated for over 21 internal and 7 external drug candidates discontinued for unacceptable incidence/magnitude of liver transaminase elevations during clinical trials or withdrawn for liver injury severity. Both hybrid scoring systems identified 70−80% Most-DILI-concern drugs, but more importantly, stratified successful/unsuccessful drug candidates for liver safety (incidence/severity of transaminase elevations and approved drug labels). Incorporating other mechanisms (reactive metabolite and cytotoxic metabolite generation and hepatic efflux transport inhibition, other than BSEP) to the HRM had minimal beneficial impact in DILI prediction/stratification. As is, the hybrid scoring system was positioned for portfolio assessments to contrast DILI risk potential of small molecule drug candidates in early clinical development. This stratified approach for DILI prediction aided decisions regarding drug candidate progression, follow-up mechanistic work, back-up selection, clinical dose selection, and due diligence assessments in favor of compounds with less implied clinical hepatotoxicity risk.