Prediction of Human Clearance Using In Silico Models with Reduced Bias.

Predicting human clearance with high accuracy from in silico-derived parameters alone is highly desirable, as it is fast, saves in vitro resources, and is animal-sparing. We derived random forest (RF) models from 1340 compounds with human intravenous pharmacokinetic (PK) data, the largest data set publicly available today. To assess the general applicability of the RF models, we systematically removed structural-therapeutic class analogues and other compounds with structural similarity from the training sets. For a quasi-prospective test set of 343 compounds, we show that RF models devoid of structurally similar compounds in the training set predict human clearance with a geometric mean fold error (GMFE) of 3.3. While the observed GMFE illustrates how difficult it is to generate a useful model that is broadly applicable, we posit that our RF models yield a more realistic assessment of how well human clearance can be predicted prospectively. We deployed the conformal prediction formalism to assess the model applicability and to determine the prediction confidence intervals for each prediction. We observed that clearance can be predicted better for renally cleared compounds than for other clearance mechanisms. We show that applying a classification model for predicting renal clearance identifies a subset of compounds for which clearance can be predicted with higher accuracy, yielding a GMFE of 2.3. In addition, our in silico RF human clearance models compared well to models derived from scaling human hepatocytes or preclinical in vivo data.

The allosteric inhibitor ABL001 enables dual targeting of BCR-ABL1.

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.

An Accurate In Vitro Prediction of Human VDss Based on the Øie-Tozer Equation and Primary Physicochemical Descriptors. 3. Analysis and Assessment of Predictivity on a Large Dataset.

We present a model for volume of distribution at steady state (VDss) prediction, via fraction unbound in tissues, from the Øie-equation as an extension of our and other authors' previous work. It is based on easily determined or computed physicochemical descriptors such as logD7.4 and fi (7.4) (cationic fraction ionized at pH 7.4) in addition to fraction unbound in plasma (fup). We had collected, as part of other work, an extensive dataset of VDss and fup values and used the descriptors above, gathered from the literature, for a preliminary assessment of the robustness of the method applied to 191 different compounds belonging to different charge classes and scaffolds. After this step, we addressed the use of easily computed physicochemical descriptors and experimentally derived fup on the same data set and compare the results between the two approaches and against the Øie-Tozer equation using in vivo data. This approach positions itself between fully computational models and scaling methods based on in vivo animal models or in vitro Kp (tissue:plasma) data utilizing model tissues. We consider it a useful and orthogonal complement to the two very diverse approaches mentioned yet requiring minimal in vitro experimental work. It offers a relatively inexpensive, rapid, intuitive, and simple way to predict VDss in humans, at a relatively early stage of the drug discovery. SIGNIFICANCE STATEMENT: This method allows the prediction of volume of distribution at steady state for small molecules in humans without the use of animal PK data because it utilizes only in vitro data. It is therefore amenable to use at early stages, simple, intuitive, animal-sparing, and quite accurate, and it may serve scaling efforts well. Furthermore, utilizing the same dataset, we show that the performance of a model using computed pKa and logD7.4, still using experimental fraction unbound in plasma, compares well with the model using experimentally derived values.

Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 1352 Drug Compounds.

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.

In Silico Prediction of Volume of Distribution in Humans. Extensive Data Set and the Exploration of Linear and Nonlinear Methods Coupled with Molecular Interaction Fields Descriptors.

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.

Benefit of Retraining pKa Models Studied Using Internally Measured Data.

The ionization state of drugs influences many pharmaceutical properties such as their solubility, permeability, and biological activity. It is therefore important to understand the structure property relationship for the acid-base dissociation constant pKa during the lead optimization process to make better-informed design decisions. Computational approaches, such as implemented in MoKa, can help with this; however, they often predict with too large error especially for proprietary compounds. In this contribution, we look at how retraining helps to greatly improve prediction error. Using a longitudinal study with data measured over 15 years in a drug discovery environment, we assess the impact of model training on prediction accuracy and look at model degradation over time. Using the MoKa software, we will demonstrate that regular retraining is required to address changes in chemical space leading to model degradation over six to nine months.

Flat patch mesh versus three-dimensional mesh (plug) for open umbilical or epigastric hernia repair. A retrospective study.

INTRODUCTION: Hernia repair using prosthetic mesh materials has become the preferred method of repair, as the recurrence rates are much lower than with conventional repair techniques. The aim of this retrospective study was to compare open small- and medium-sized abdominal wall hernia repair with flat patch mesh versus three-dimensional mesh (plug) in terms of recurrence and complication rates. METHODS: The medical records of 300 patients who underwent abdominal wall hernia repair using flat patch mesh versus three-dimensional mesh between January 2010 to December 2015 were reviewed. All patients were followed up after 1 month, 3 month and 1 year. The rate of recurrence, and short-term postoperative complications such as incidence of Surgical Site Infections (SSIs), hematoma and seroma were evaluated. RESULTS: Short-term follow-up data were available for all patients. The first group was composed of 150 patients that were treated with a flat polypropylene mesh (68% presened umbilical hernia and 32% presented epigastric hernia). The second group was composed of 150 patients that were treated with a three-dimensional polypropylene mesh (60% presented umbilical hernia and 40% presented epigastric hernia). The majority of postoperative (1-month) complications were wound related, representing superficial SSI or seroma. Our results showed a statistically significant reduction of SSIs [3 (2%) vs 13 (8.6%); p = 0.038] and seroma [2 (1.3%) vs 12 (8%); p = 0.030] in the group of patients treated with plugs compared to flat-mesh group. There was no statistically significant difference in hernia recurrences. DISCUSSION: Usage three-dimensional mesh for open small- and medium-sized umbilical or epigastric hernia repair represents a feasible and safe technique that significantly lowers the incidence of complications such as SSIs and seroma. Furthermore, compared to flat patch mesh, plugs displayed non-inferiority in terms recurrence. Further, well-designed clinical trials could be realized to investigate possible applications of plugs in treatment of small- and medium-sized umbilical and epigastric hernias. KEY WORDS: Mesh, Umbilical Hernia.

Oxidative ipso substitution of 2,4-difluoro-benzylphthalazines: identification of a rare stable quinone methide and subsequent GSH conjugate.

In vitro metabolite identification and GSH trapping studies in human liver microsomes were conducted to understand the bioactivation potential of compound 1 [2-(6-(4-(4-(2,4-difluorobenzyl)phthalazin-1-yl)piperazin-1-yl)pyridin-3-yl)propan-2-ol], an inhibitor of the Hedgehog pathway. The results revealed the formation of a unique, stable quinone methide metabolite (M1) via ipso substitution of a fluorine atom and subsequent formation of a GSH adduct (M2). The stability of this metabolite arises from extensive resonance-stabilized conjugation of the substituted benzylphthalazine moiety. Cytochrome P450 (P450) phenotyping studies revealed that the formation of M1 and M2 were NADPH-dependent and primarily catalyzed by CYP3A4 among the studied P450 isoforms. In summary, an unusual and stable quinone methide metabolite of compound 1 was identified, and a mechanism was proposed for its formation via an oxidative ipso substitution.

In silico prediction of total human plasma clearance.

The prediction of the total human plasma clearance of novel chemical entities continues to be of paramount importance in drug design and optimization, because it impacts both dose size and dose regimen. Although many in vivo and in vitro methods have been proposed, a well-constructed, well-validated, and less resource-intensive computational tool would still be very useful in an iterative compound design cycle. A new completely in silico linear PLS (partial least-squares) model to predict the human plasma clearance was built on the basis of a large data set of 754 compounds using physicochemical descriptors and structural fragments, the latter able to better represent biotransformation processes. The model has been validated using the "ELASTICO" approach (Enhanced Leave Analog-Structural, Therapeutic, Ionization Class Out) based on ten therapeutic/structural analog classes. The model yields a geometric mean fold error (GMFE) of 2.1 and a percentage of compounds predicted within 2- and 3-fold error of 59% and 80%, respectively, showing an improved performance when compared with previous published works in predicting clearance of neutral compounds, and a very good performance with ionized molecules at pH 7.5, able to compare favorably with fairly accurate in vivo methods.

In Silico Models of Human PK Parameters. Prediction of Volume of Distribution Using an Extensive Data Set and a Reduced Number of Parameters.

A novel, descriptor-parsimonious in silico model to predict human VDss (volume of distribution at steady-state) has been derived and thoroughly tested in a quasi-prospective regimen using an independent test set of 213 compounds. The model performs on par with a former benchmark model that relied on far more descriptors. As a result, the new random forest model relying on only six descriptors allows for interpretations that help chemists to design compounds with desired human VDss values. A comparison of in silico predictions of VDss with models using in vitro derived descriptors or in vivo scaling methods supports the strength of the in-silico approach, considering its resource- and animal-sparing nature. The strong performance of the in silico VDss models on structurally novel compounds supports the high degree of confidence that can be placed in using in silico human VDss predictions for compound design and human dose predictions.

Use of the Øie-Tozer model in understanding mechanisms and determinants of drug distribution.

Volume of distribution (VD) is a key pharmacokinetic property that together with clearance determines the half-life or residence time of drug in the body. It is commonly expressed as steady-state volume of distribution VD(ss) with a physiological basis for its understanding developed by Øie and Tozer in 1979. The Øie-Tozer equation uses terms for plasma protein binding (f(up)), tissue binding (f(ut)), and the extravascular/intravascular ratio of albumin as well as constants for the volumes of plasma, extracellular fluid, and tissue. We explored this model using a data set of 553 drugs for which VD(ss) and plasma protein binding were available in humans. Eighteen percent of cases (102 compounds) did not obey the Øie-Tozer model, with the rearranged equation giving an aberrant f(ut) value (f(ut) < 0 or f(ut) > 1), in particular for compounds with VD(ss) < 0.6 l/kg and f(up) > 0.1. Further analysis of this group of compounds revealed patterns in physicochemical attributes with a high proportion exemplified by logP less than 0 (i.e., very hydrophilic), polar surface area >150 A(2), and a difference between logP and logD >2.5. In addition there was a high representation of certain drug classes including anti-infectives as well as neuromuscular blockers and contrast agents. The majority of compounds were also found to have literature evidence, implicating active transport processes in their disposition. This analysis provides some important insights for pharmacokinetic optimization in this particular chemical space, as well as in the application of the Øie-Tozer model for predicting volume of distribution in humans.

Discovery of a Brain-Penetrant ATP-Competitive Inhibitor of the Mechanistic Target of Rapamycin (mTOR) for CNS Disorders.

Recent clinical evaluation of everolimus for seizure reduction in patients with tuberous sclerosis complex (TSC), a disease with overactivated mechanistic target of rapamycin (mTOR) signaling, has demonstrated the therapeutic value of mTOR inhibitors for central nervous system (CNS) indications. Given that everolimus is an incomplete inhibitor of the mTOR function, we sought to develop a new mTOR inhibitor that has improved properties and is suitable for CNS disorders. Starting from an in-house purine-based compound, optimization of the physicochemical properties of a thiazolopyrimidine series led to the discovery of the small molecule 7, a potent and selective brain-penetrant ATP-competitive mTOR inhibitor. In neuronal cell-based models of mTOR hyperactivity, 7 corrected the mTOR pathway activity and the resulting neuronal overgrowth phenotype. The new mTOR inhibitor 7 showed good brain exposure and significantly improved the survival rate of mice with neuronal-specific ablation of the Tsc1 gene. These results demonstrate the potential utility of this tool compound to test therapeutic hypotheses that depend on mTOR hyperactivity in the CNS.

Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds.

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.

Discovery of LSZ102, a Potent, Orally Bioavailable Selective Estrogen Receptor Degrader (SERD) for the Treatment of Estrogen Receptor Positive Breast Cancer.

In breast cancer, estrogen receptor alpha (ERα) positive cancer accounts for approximately 74% of all diagnoses, and in these settings, it is a primary driver of cell proliferation. Treatment of ERα positive breast cancer has long relied on endocrine therapies such as selective estrogen receptor modulators, aromatase inhibitors, and selective estrogen receptor degraders (SERDs). The steroid-based anti-estrogen fulvestrant (5), the only approved SERD, is effective in patients who have not previously been treated with endocrine therapy as well as in patients who have progressed after receiving other endocrine therapies. Its efficacy, however, may be limited due to its poor physicochemical properties. We describe the design and synthesis of a series of potent benzothiophene-containing compounds that exhibit oral bioavailability and preclinical activity as SERDs. This article culminates in the identification of LSZ102 (10), a compound in clinical development for the treatment of ERα positive breast cancer.

Discovery of Asciminib (ABL001), an Allosteric Inhibitor of the Tyrosine Kinase Activity of BCR-ABL1.

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.

In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices. An Industry Perspective from the International Consortium for Innovation through Quality in Pharmaceutical Development.

In silico tools to investigate absorption, distribution, metabolism, excretion, and pharmacokinetics (ADME-PK) properties of new chemical entities are an integral part of the current industrial drug discovery paradigm. While many companies are active in the field, scientists engaged in this area do not necessarily share the same background and have limited resources when seeking guidance on how to initiate and maintain an in silico ADME-PK infrastructure in an industrial setting. This work summarizes the views of a group of industrial in silico and experimental ADME scientists, participating in the In Silico ADME Working Group, a subgroup of the International Consortium for Innovation through Quality in Pharmaceutical Development (IQ) Drug Metabolism Leadership Group. This overview on the benefits, caveats, and impact of in silico ADME-PK should serve as a resource for medicinal chemists, computational chemists, and DMPK scientists working in drug design to increase their knowledge in the area.

Discovery of an Acrylic Acid Based Tetrahydroisoquinoline as an Orally Bioavailable Selective Estrogen Receptor Degrader for ERα+ Breast Cancer.

Tetrahydroisoquinoline 40 has been identified as a potent ERα antagonist and selective estrogen receptor degrader (SERD), exhibiting good oral bioavailability, antitumor efficacy, and SERD activity in vivo. We outline the discovery and chemical optimization of the THIQ scaffold leading to THIQ 40 and showcase the racemization of the scaffold, pharmacokinetic studies in preclinical species, and the in vivo efficacy of THIQ 40 in a MCF-7 human breast cancer xenograft model.

A hybrid mixture discriminant analysis-random forest computational model for the prediction of volume of distribution of drugs in human.

A computational approach is described that can predict the VD(ss) of new compounds in humans, with an accuracy of within 2-fold of the actual value. A dataset of VD values for 384 drugs in humans was used to train a hybrid mixture discriminant analysis-random forest (MDA-RF) model using 31 computed descriptors. Descriptors included terms describing lipophilicity, ionization, molecular volume, and various molecular fragments. For a test set of 23 proprietary compounds not used in model construction, the geometric mean fold-error (GMFE) was 1.78-fold (+/-11.4%). The model was also tested using a leave-class out approach wherein subsets of drugs based on therapeutic class were removed from the training set of 384, the model was recast, and the VD(ss) values for each of the subsets were predicted. GMFE values ranged from 1.46 to 2.94-fold, depending on the subset. Finally, for an additional set of 74 compounds, VD(ss) predictions made using the computational model were compared to predictions made using previously described methods dependent on animal pharmacokinetic data. Computational VD(ss) predictions were, on average, 2.13-fold different from the VD(ss) predictions from animal data. The computational model described can predict human VD(ss) with an accuracy comparable to predictions requiring substantially greater effort and can be applied in place of animal experimentation.

Systematic Evaluation of Wajima Superposition (Steady-State Concentration to Mean Residence Time) in the Estimation of Human Intravenous Pharmacokinetic Profile.

We present a systematic evaluation of the Wajima superpositioning method to estimate the human intravenous (i.v.) pharmacokinetic (PK) profile based on a set of 54 marketed drugs with diverse structure and range of physicochemical properties. We illustrate the use of average of "best methods" for the prediction of clearance (CL) and volume of distribution at steady state (VDss) as described in our earlier work (Lombardo F, Waters NJ, Argikar UA, et al. J Clin Pharmacol. 2013;53(2):178-191; Lombardo F, Waters NJ, Argikar UA, et al. J Clin Pharmacol. 2013;53(2):167-177). These methods provided much more accurate prediction of human PK parameters, yielding 88% and 70% of the prediction within 2-fold error for VDss and CL, respectively. The prediction of human i.v. profile using Wajima superpositioning of rat, dog, and monkey time-concentration profiles was tested against the observed human i.v. PK using fold error statistics. The results showed that 63% of the compounds yielded a geometric mean of fold error below 2-fold, and an additional 19% yielded a geometric mean of fold error between 2- and 3-fold, leaving only 18% of the compounds with a relatively poor prediction. Our results showed that good superposition was observed in any case, demonstrating the predictive value of the Wajima approach, and that the cause of poor prediction of human i.v. profile was mainly due to the poorly predicted CL value, while VDss prediction had a minor impact on the accuracy of human i.v. profile prediction.

Antibacterial and Solubility Optimization of Thiomuracin A.

Synthetic studies of the antimicrobial secondary metabolite thiomuracin A (1) provided access to analogues in the Northern region (C2-C10). Selective hydrolysis of the C10 amide of lead compound 2 and subsequent derivatization led to novel carbon- and nitrogen-linked analogues (e.g., 3) which improved antibacterial potency across a panel of Gram-positive organisms. In addition, congeners with improved physicochemical properties were identified which proved efficacious in murine sepsis and hamster C. difficile models of disease. Optimal efficacy in the hamster model of C. difficile was achieved with compounds that possessed both potent antibacterial activity and high aqueous solubility.