Discovery of AZD4573, a Potent and Selective Inhibitor of CDK9 That Enables Short Duration of Target Engagement for the Treatment of Hematological Malignancies.

A CDK9 inhibitor having short target engagement would enable a reduction of Mcl-1 activity, resulting in apoptosis in cancer cells dependent on Mcl-1 for survival. We report the optimization of a series of amidopyridines (from compound 2), focusing on properties suitable for achieving short target engagement after intravenous administration. By increasing potency and human metabolic clearance, we identified compound 24, a potent and selective CDK9 inhibitor with suitable predicted human pharmacokinetic properties to deliver transient inhibition of CDK9. Furthermore, the solubility of 24 was considered adequate to allow i.v. formulation at the anticipated effective dose. Short-term treatment with compound 24 led to a rapid dose- and time-dependent decrease of pSer2-RNAP2 and Mcl-1, resulting in cell apoptosis in multiple hematological cancer cell lines. Intermittent dosing of compound 24 demonstrated efficacy in xenograft models derived from multiple hematological tumors. Compound 24 is currently in clinical trials for the treatment of hematological malignancies.

Translational model to predict pulmonary pharmacokinetics and efficacy in man for inhaled bronchodilators.

Translational pharmacokinetic (PK) models are needed to describe and predict drug concentration-time profiles in lung tissue at the site of action to enable animal-to-man translation and prediction of efficacy in humans for inhaled medicines. Current pulmonary PK models are generally descriptive rather than predictive, drug/compound specific, and fail to show successful cross-species translation. The objective of this work was to develop a robust compartmental modeling approach that captures key features of lung and systemic PK after pulmonary administration of a set of 12 soluble drugs containing single basic, dibasic, or cationic functional groups. The model is shown to allow translation between animal species and predicts drug concentrations in human lungs that correlate with the forced expiratory volume for different classes of bronchodilators. Thus, the pulmonary modeling approach has potential to be a key component in the prediction of human PK, efficacy, and safety for future inhaled medicines.

Discovery of a potent respiratory syncytial virus RNA polymerase inhibitor.

Targeting viral polymerases has been a proven and attractive strategy for antiviral drug discovery. Herein we describe our effort in improving the antiviral activity and physical properties of a series of benzothienoazepine compounds as respiratory syncytial virus (RSV) RNA polymerase inhibitors. The antiviral activity and spectrum of this class was significantly improved by exploring the amino substitution of the pyridine ring, resulting in the discovery of the most potent RSV A polymerase inhibitors reported to date.

Intratracheal and oral administration of SM-276001: a selective TLR7 agonist, leads to antitumor efficacy in primary and metastatic models of cancer.

Topical TLR7 agonists such as imiquimod are highly effective for the treatment of dermatological malignancies; however, their efficacy in the treatment of nondermatological tumors has been less successful. We report that oral administration of the novel TLR7-selective small molecule agonist; SM-276001, leads to the induction of an inflammatory cytokine and chemokine milieu and to the activation of a diverse population of immune effector cells including T and B lymphocytes, NK and NKT cells. Oral administration of SM-276001 leads to the induction of IFNα, TNFα and IL-12p40 and a reduction in tumor burden in the Balb/c syngeneic Renca and CT26 models. Using the OV2944-HM-1 model of ovarian cancer which spontaneously metastasizes to the lungs following subcutaneous implantation, we evaluated the efficacy of intratracheal and oral administration of SM-276001 in an adjuvant setting following surgical resection of the primary tumor. We show that both oral and intratracheal TLR7 therapy can reduce the frequency of pulmonary metastasis, and metastasis to the axillary lymph nodes. These results demonstrate that SM-276001 is a potent selective TLR7 agonist that can induce antitumor immune responses when dosed either intratracheally or orally.

The use of HepaRG and human hepatocyte data in predicting CYP induction drug-drug interactions via static equation and dynamic mechanistic modelling approaches.

The method of predicting CYP induction drug-drug interactions (DDIs) from a relative induction score (RIS) calibration has been developed to provide a novel model facilitating predictions for any CYP-inducer substrate combination by inclusion of parameters such as the fraction of hepatic clearance mediated by a specific CYP and fraction of the dose escaping intestinal extraction. In vitro HepaRG CYP3A4 induction data were used as a basis for the approach and a large number of DDIs were well predicted. Primary human hepatocyte data were also used to make predictions, using the HepaRG calibration as a foundation. Similar predictive accuracy suggests that HepaRG and primary hepatocyte data can be used inter-changeably within the same laboratory. A comparison of this 'indirect' calibration method with a direct in vitro-in vivo scaling approach was made and investigations undertaken to define the most appropriate in vivo inducer concentration to use. Additionally, a reasonably effective prediction model based on F(2) (the concentration of inducer taken to increase the CYP mRNA 2-fold above background) was established. An accurate prediction for the CYP1A2-dependent omeprazole-caffeine interaction was also made, demonstrating that the methods are useful for the evaluation of DDIs from induction involving mechanisms other than PXR activation. Finally, a dynamic mechanistic model accounting for the simultaneous influence of CYP induction and reversible and irreversible CYP inhibition in both the liver and intestine was written to provide a prediction of the overall DDI when several interactions occur concurrently. The rationale for using the various models described, alongside commercially available prediction tools, at various stages of the drug discovery process is described.

Mechanism-based inhibition of cytochrome P450 enzymes: an evaluation of early decision making in vitro approaches and drug-drug interaction prediction methods.

The ability to use in vitro human cytochrome P450 (CYP) time-dependent inhibition (TDI) data for in vivo drug-drug interaction (DDI) predictions should be viewed as a prerequisite to generating the data. Important terms in making such predictions are k(inact) and K(I) but first-line screening assays typically involve characterisation of an IC(50) value or a time dependent shift in IC(50). In the work presented here, two key screening methods from the scientific literature were appraised both in terms of practicality and quality of k(inact)/K(I) estimation. The utility of TDI screening data in DDI predictions was investigated and particular reference given to a simple DDI simulation model based on a spreadsheet that calculates the systemic exposure of unbound inhibitor drug following the input of human pharmacokinetic parameters. Using several clinical mechanism-based CYP DDI examples, the effectiveness of the approach was assessed and compared to other widely available approaches (a simple algorithm that employs a single in vivo unbound inhibitor concentration, a seven-compartment physiologically based pharmacokinetic (PBPK) model that defines the extent of interaction as a result of hepatic inhibitor concentrations and the commercially available software SimCYP). All the methods gave predictions that compared favourably with the observed DDIs, but various advantages and disadvantages of each were also given full consideration. The new model facilitates rapid sensitivity analysis (parameters can be easily input and altered to give a visual representation of the impact on the active enzyme concentration) and it was therefore used to derive "rules of thumb" demonstrating the relationship between extent of DDI, time-dependent IC(50) and dose for typical acidic and basic drugs. Additionally, a TDI decision tree linking into reactive metabolite investigations is proposed for use in a Drug Discovery setting.