We report a trend analysis of human intravenous pharmacokinetic data on a data set of 1352 drugs. The aim in building this data set and its detailed analysis was to provide, as in the previous case ...published in 2008, an extended, robust, and accurate resource that could be applied by drug metabolism, clinical pharmacology, and medicinal chemistry scientists to a variety of scaling approaches. All in vivo data were obtained or derived from original references, either through the literature or regulatory agency reports, exclusively from studies utilizing intravenous administration. Plasma protein binding data were collected from other available sources to supplement these pharmacokinetic data. These parameters were analyzed concurrently with a range of physicochemical properties, and resultant trends and patterns within the data are presented. In addition, the date of first disclosure of each molecule was reported and the potential "temporal" impact on data trends was analyzed. The findings reported here are consistent with earlier described trends between pharmacokinetic behavior and physicochemical properties. Furthermore, the availability of a large data set of pharmacokinetic data in humans will be important to further pursue analyses of physicochemical properties, trends, and modeling efforts and should propel our deeper understanding (especially in terms of clearance) of the absorption, distribution, metabolism, and excretion behavior of drug compounds.
Experimental and computational approaches to estimate solubility and permeability in discovery and development settings are described. In the discovery setting ‘the rule of 5’ predicts that poor ...absorption or permeation is more likely when there are more than 5 H-bond donors, 10 H-bond acceptors, the molecular weight (MWT) is greater than 500 and the calculated Log P (CLogP) is greater than 5 (or MlogP>4.15). Computational methodology for the rule-based Moriguchi Log P (MLogP) calculation is described. Turbidimetric solubility measurement is described and applied to known drugs. High throughput screening (HTS) leads tend to have higher MWT and Log P and lower turbidimetric solubility than leads in the pre-HTS era. In the development setting, solubility calculations focus on exact value prediction and are difficult because of polymorphism. Recent work on linear free energy relationships and Log P approaches are critically reviewed. Useful predictions are possible in closely related analog series when coupled with experimental thermodynamic solubility measurements.
Chronic myelogenous leukemia (CML) arises from the constitutive activity of the BCR-ABL1 oncoprotein. Tyrosine kinase inhibitors (TKIs) that target the ATP-binding site have transformed CML into a ...chronic manageable disease. However, some patients develop drug resistance due to ATP-site mutations impeding drug binding. We describe the discovery of asciminib (ABL001), the first allosteric BCR-ABL1 inhibitor to reach the clinic. Asciminib binds to the myristate pocket of BCR-ABL1 and maintains activity against TKI-resistant ATP-site mutations. Although resistance can emerge due to myristate-site mutations, these are sensitive to ATP-competitive inhibitors so that combinations of asciminib with ATP-competitive TKIs suppress the emergence of resistance. Fragment-based screening using NMR and X-ray yielded ligands for the myristate pocket. An NMR-based conformational assay guided the transformation of these inactive ligands into ABL1 inhibitors. Further structure-based optimization for potency, physicochemical, pharmacokinetic, and drug-like properties, culminated in asciminib, which is currently undergoing clinical studies in CML patients.
Chronic myeloid leukaemia (CML) is driven by the activity of the BCR-ABL1 fusion oncoprotein. ABL1 kinase inhibitors have improved the clinical outcomes for patients with CML, with over 80% of ...patients treated with imatinib surviving for more than 10 years. Second-generation ABL1 kinase inhibitors induce more potent molecular responses in both previously untreated and imatinib-resistant patients with CML. Studies in patients with chronic-phase CML have shown that around 50% of patients who achieve and maintain undetectable BCR-ABL1 transcript levels for at least 2 years remain disease-free after the withdrawal of treatment. Here we characterize ABL001 (asciminib), a potent and selective allosteric ABL1 inhibitor that is undergoing clinical development testing in patients with CML and Philadelphia chromosome-positive (Ph
) acute lymphoblastic leukaemia. In contrast to catalytic-site ABL1 kinase inhibitors, ABL001 binds to the myristoyl pocket of ABL1 and induces the formation of an inactive kinase conformation. ABL001 and second-generation catalytic inhibitors have similar cellular potencies but distinct patterns of resistance mutations, with genetic barcoding studies revealing pre-existing clonal populations with no shared resistance between ABL001 and the catalytic inhibitor nilotinib. Consistent with this profile, acquired resistance was observed with single-agent therapy in mice; however, the combination of ABL001 and nilotinib led to complete disease control and eradicated CML xenograft tumours without recurrence after the cessation of treatment.
We present herein a compilation and trend analysis of human i.v. pharmacokinetic data on 670 drugs representing, to our knowledge, the largest publicly available set of human clinical pharmacokinetic ...data. This data set provides the drug metabolism scientist with a robust and accurate resource suitable for a number of applications, including in silico modeling, in vitro-in vivo scaling, and physiologically based pharmacokinetic approaches. Clearance, volume of distribution at steady state, mean residence time, and terminal phase half-life were obtained or derived from original references exclusively from studies utilizing i.v. administration. Plasma protein binding data were collected from other sources to supplement these pharmacokinetic data. These parameters were analyzed concurrently with a range of simple physicochemical descriptors, and resultant trends and patterns within the data are presented. Our findings with this much expanded data set were consistent with earlier described notions of trends between physicochemical properties and pharmacokinetic behavior. These observations and analyses, along with the large database of human pharmacokinetic data, should enable future efforts aimed toward developing quantitative structure-pharmacokinetic relationships and improving our understanding of the relationship between fundamental chemical characteristics and drug disposition.
SHP2 is a nonreceptor protein tyrosine phosphatase (PTP) encoded by the PTPN11 gene involved in cell growth and differentiation via the MAPK signaling pathway. SHP2 also purportedly plays an ...important role in the programmed cell death pathway (PD-1/PD-L1). Because it is an oncoprotein associated with multiple cancer-related diseases, as well as a potential immunomodulator, controlling SHP2 activity is of significant therapeutic interest. Recently in our laboratories, a small molecule inhibitor of SHP2 was identified as an allosteric modulator that stabilizes the autoinhibited conformation of SHP2. A high throughput screen was performed to identify progressable chemical matter, and X-ray crystallography revealed the location of binding in a previously undisclosed allosteric binding pocket. Structure-based drug design was employed to optimize for SHP2 inhibition, and several new protein–ligand interactions were characterized. These studies culminated in the discovery of 6-(4-amino-4-methylpiperidin-1-yl)-3-(2,3-dichlorophenyl)pyrazin-2-amine (SHP099, 1), a potent, selective, orally bioavailable, and efficacious SHP2 inhibitor.
A comprehensive analysis on the prediction of human clearance based on intravenous pharmacokinetic data from rat, dog, and monkey for approximately 400 compounds was undertaken. This data set has ...been carefully compiled from literature reports and expanded with some in‐house determinations for plasma protein binding and rat clearance. To the authors— knowledge, this is the largest publicly available data set. The present examination offers a comparison of 37 different methods for prediction of human clearance across compounds of diverse physicochemical properties. Furthermore, this work demonstrates the application of each prediction method to each charge class of the compounds, thus presenting an additional dimension to prediction of human pharmacokinetics. In general, the observations suggest that methods employing monkey clearance values and a method incorporating differences in plasma protein binding between rat and human yield the best overall predictions as suggested by approximately 60% compounds within 2‐fold geometric mean‐fold error. Other single‐species scaling or proportionality methods incorporating the fraction unbound in the corresponding preclinical species for prediction of free clearance in human were generally unsuccessful.
The authors present a comprehensive analysis on the estimation of volume of distribution at steady state (VDss) in human based on rat, dog, and monkey data on nearly 400 compounds for which there are ...also associated human data. This data set, to the authors— knowledge, is the largest publicly available, has been carefully compiled from literature reports, and was expanded with some in‐house determinations such as plasma protein binding data. This work offers a good statistical basis for the evaluation of applicable prediction methods, their accuracy, and some methods‐dependent diagnostic tools. The authors also grouped the compounds according to their charge classes and show the applicability of each method considered to each class, offering further insight into the probability of a successful prediction. Furthermore, they found that the use of fraction unbound in plasma, to obtain unbound volume of distribution, is generally detrimental to accuracy of several methods, and they discuss possible reasons. Overall, the approach using dog and monkey data in the íie‐Tozer equation offers the highest probability of success, with an intrinsic diagnostic tool based on aberrant values (<0 or >1) for the calculated fraction unbound in tissue. Alternatively, methods based on dog data (single‐species scaling) and rat and dog data (íie‐Tozer equation with 2 species or multiple regression methods) may be considered reasonable approaches while not requiring data in nonhuman primates.
Experimental and computational approaches to estimate solubility and permeability in discovery and development settings are described. In the discovery setting ‘the rule of 5’ predicts that poor ...absorption or permeation is more likely when there are more than 5 Hbond donors, 10 Hbond acceptors, the molecular weight (MWT) is greater than 500 and the calculated Log P (CLogP) is greater than 5 (or MlogP > 4.15). Computational methodology for the rule-based Moriguchi Log P (MLogP) calculation is described. Turbidimetric solubility measurement is described and applied to known drugs. High throughput screening (HTS) leads tend to have higher MWT and Log P and lower turbidimetric solubility than leads in the pre-HTS era. In the development setting, solubility calculations focus on exact value prediction and are difficult because of polymorphism. Recent work on linear free energy relationships and Log P approaches are critically reviewed. Useful predictions are possible in closely related analog series when coupled with experimental thermodynamic solubility measurements.
We present three in silico volume of distribution at steady state (VDss) models generated on a training set comprising 1096 compounds, which goes well beyond the conventional drug space delineated by ...the Rule of 5 or similar approaches. We have performed a careful selection of descriptors and kept a homogeneous Molecular Interaction Field-based descriptor set and linear (Partial Least Squares, PLS) and nonlinear (Random Forest, RF) models. We have tested the models, which we deem orthogonal in nature due to different descriptors and statistical approaches, with good results. In particular we tested the RF model, via a leave-class-out approach and by using a set of 34 additional compounds not used for training. We report comparable results against in vivo scaling approaches with geometric mean-fold error at or below 2 (for a set of 60 compounds with animal data available) and discuss the predictive performance based on the ionization states of the compounds. Lastly, we report the findings using a two-tier approach (classification followed by regression) based on VDss ranges, in an attempt to improve the prediction of compounds with very high VDss. We would recommend, overall, the RF model, with 33 descriptors, as the primary choice for VDss prediction in humans.
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