Beyond Rule of Five and PROTACs in Modern Drug Discovery: Polarity Reducers, Chameleonicity, and the Evolving Physicochemical Landscape.

Developing orally bioavailable drugs demands an understanding of absorption in early drug development. Traditional methods and physicochemical properties optimize absorption for rule of five (Ro5) compounds; beyond rule of five (bRo5) drugs necessitate advanced tools like the experimental measure of exposed polarity (EPSA) and the AbbVie multiparametric score (AB-MPS). Analyzing AB-MPS and EPSA against ∼1000 compounds with human absorption data and ∼10,000 AbbVie tool compounds (∼1000 proteolysis targeting chimeras or PROTACs, ∼7000 Ro5s, and ∼2000 bRo5s) revealed new patterns of physicochemical trends. We introduced a high-throughput "polarity reduction" descriptor: ETR, the EPSA-to-topological polar surface area (TPSA) ratio, highlights unique bRo5 and PROTAC subsets for specialized drug design strategies for effective absorption. Our methods and guidelines refine drug design by providing innovative in vitro approaches, enhancing physicochemical property optimization, and enabling accurate predictions of intestinal absorption in the complex bRo5 domain.

Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm.

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno-oncology (IO) the aim is to direct the patient's own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD-L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug-development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds' pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities.

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.

Discovery of ABT-957: 1-Benzyl-5-oxopyrrolidine-2-carboxamides as selective calpain inhibitors with enhanced metabolic stability.

Aberrant activation of calpain has been observed in various pathophysiological disorders including neurodegenerative diseases such as Alzheimer's Disease. Here we describe our efforts on ketoamide-based 1-benzyl-5-oxopyrrolidine-2-carboxamides as a novel series of highly selective calpain inhibitors mitigating the metabolic liability of carbonyl reduction. The most advanced compound from this new series, namely A-1212805 (ABT-957, Alicapistat) proceeded to clinical phase I studies.

Quantitative Systems Pharmacology Model for Alzheimer Disease Indicates Targeting Sphingolipid Dysregulation as Potential Treatment Option.

Alzheimer disease (AD) is a devastating neurodegenerative disorder with high unmet medical need. Drug development is hampered by limited understanding of the disease and its driving factors. Quantitative Systems Pharmacology (QSP) modeling provides a comprehensive quantitative framework to evaluate the relevance of biological mechanisms in the context of disease and to predict the efficacy of novel treatments. Here, we report a QSP model for AD with a particular focus on investigating the relevance of dysregulation of cholesterol and sphingolipids. We show that our model captures the modulation of several biomarkers in subjects with AD, as well as the response to pharmacological interventions. We evaluate the impact of targeting the sphingosine-1-phosphate 5 receptor (S1PR5) as a potential novel treatment option for AD, and model predictions increase our confidence in this novel disease pathway. Future applications for the QSP model are in validation of further targets and identification of potential treatment response biomarkers.

Mitigating the Metabolic Liability of Carbonyl Reduction: Novel Calpain Inhibitors with P1' Extension.

Dysregulation of calpains 1 and 2 has been implicated in a variety of pathological disorders including ischemia/reperfusion injuries, kidney diseases, cataract formation, and neurodegenerative diseases such as Alzheimer's disease (AD). 2-(3-Phenyl-1H)-pyrazol-1-yl)nicotinamides represent a series of novel and potent calpain inhibitors with high selectivity and in vivo efficacy. However, carbonyl reduction leading to the formation of the inactive hydroxyamide was identified as major metabolic liability in monkey and human, a pathway not reflected by routine absorption, distribution, metabolism, and excretion (ADME) assays. Using cytosolic clearance as a tailored in vitro ADME assay coupled with in vitro hepatocyte metabolism enabled the identification of analogues with enhanced stability against carbonyl reduction. These efforts led to the identification of P1' modified calpain inhibitors with significantly improved pharmacokinetic profile including P1' N-methoxyamide 23 as potential candidate compound for non-central nervous system indications.

Mathematical and Experimental Validation of Flux Dialysis Method: An Improved Approach to Measure Unbound Fraction for Compounds with High Protein Binding and Other Challenging Properties.

A flux dialysis method to measure unbound fraction (fu) of compounds with high protein binding and other challenging properties was tested and validated. This method is based on the principle that the initial flux rate of a compound through a size-excluding dialysis membrane is proportional to the product of the compound initial concentration, fu, and unbound dialysis membrane permeability (Pmem). Therefore, fu can be determined from the initial concentration and flux rate, assuming membrane Pmem is known. Compound initial flux rates for 14 compounds were determined by dialyzing human plasma containing compound (donor side) versus compound-free plasma (receiver side) and measuring the rate of compound appearance into the receiver side. Eleven compounds had known fu values obtained from conventional methods (ranging from 0.000013 to 0.22); three compounds (bedaquiline, lapatinib, and pibrentasvir) had previously qualified fu values (e.g., <0.001).Pmem estimated from flux rates and known fu values did not meaningfully differ among the compounds and were consistent with previously published values, indicating that Pmem is a constant for the dialysis membrane. This Pmem constant and the individual compound flux rates were used to calculate fu values. The flux dialysis fu values for the 11 compounds were in good agreement with their reported fu values (all within 2.5-fold; R2 = 0.980), confirming the validity of the method. Furthermore, the flux dialysis method allowed discrete fu to be estimated for the three compounds with previously qualified fu Theoretical and experimental advantages of the flux dialysis method over other dialysis-based protein binding methods are discussed.

Preclinical QSP Modeling in the Pharmaceutical Industry: An IQ Consortium Survey Examining the Current Landscape.

A cross-industry survey was conducted to assess the landscape of preclinical quantitative systems pharmacology (QSP) modeling within pharmaceutical companies. This article presents the survey results, which provide insights on the current state of preclinical QSP modeling in addition to future opportunities. Our results call attention to the need for an aligned definition and consistent terminology around QSP, yet highlight the broad applicability and benefits preclinical QSP modeling is currently delivering.

Mechanistic Physiologically Based Pharmacokinetic Modeling of the Dissolution and Food Effect of a Biopharmaceutics Classification System IV Compound-The Venetoclax Story.

Venetoclax, a selective B-cell lymphoma-2 inhibitor, is a biopharmaceutics classification system class IV compound. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to mechanistically describe absorption and disposition of an amorphous solid dispersion formulation of venetoclax in humans. A mechanistic PBPK model was developed incorporating measured amorphous solubility, dissolution, metabolism, and plasma protein binding. A middle-out approach was used to define permeability. Model predictions of oral venetoclax pharmacokinetics were verified against clinical studies of fed and fasted healthy volunteers, and clinical drug interaction studies with strong CYP3A inhibitor (ketoconazole) and inducer (rifampicin). Model verification demonstrated accurate prediction of the observed food effect following a low-fat diet. Ratios of predicted versus observed Cmax and area under the curve of venetoclax were within 0.8- to 1.25-fold of observed ratios for strong CYP3A inhibitor and inducer interactions, indicating that the venetoclax elimination pathway was correctly specified. The verified venetoclax PBPK model is one of the first examples mechanistically capturing absorption, food effect, and exposure of an amorphous solid dispersion formulated compound. This model allows evaluation of untested drug-drug interactions, especially those primarily occurring in the intestine, and paves the way for future modeling of biopharmaceutics classification system IV compounds.

Translational pharmacokinetic-pharmacodynamic analysis in the pharmaceutical industry: an IQ Consortium PK-PD Discussion Group perspective.

With inadequate efficacy being the primary cause for the attrition of drug candidates in clinical development, the need to better predict clinical efficacy earlier in the drug development process has increased in importance in the pharmaceutical industry. Here, we review current applications of translational pharmacokinetic-pharmacodynamic (PK-PD) modeling of preclinical data in the pharmaceutical industry, including best practices. Preclinical translational PK-PD modeling has been used in many therapeutic areas and has been impactful to drug development. The role of preclinical translational PK-PD modeling in drug discovery and development will continue to evolve and broaden, given that its broad implementation in the pharmaceutical industry is relatively recent and many opportunities still exist for its further application.

Mechanisms and Predictions of Drug-Drug Interactions of the Hepatitis C Virus Three Direct-Acting Antiviral Regimen: Paritaprevir/Ritonavir, Ombitasvir, and Dasabuvir.

To assess drug-drug interaction (DDI) potential for the three direct-acting antiviral (3D) regimen of ombitasvir, dasabuvir, and paritaprevir, in vitro studies profiled drug-metabolizing enzyme and transporter interactions. Using mechanistic static and dynamic models, DDI potential was predicted for CYP3A, CYP2C8, UDP-glucuronosyltransferase (UGT) 1A1, organic anion-transporting polypeptide (OATP) 1B1/1B3, breast cancer resistance protein (BCRP), and P-glycoprotein (P-gp). Perpetrator static model DDI predictions for metabolizing enzymes were within 2-fold of the clinical observations, but additional physiologically based pharmacokinetic modeling was necessary to achieve the same for drug transporters. When perpetrator interactions were assessed, ritonavir was responsible for the strong increase in exposure of sensitive CYP3A substrates, whereas paritaprevir (an OATP1B1/1B3 inhibitor) greatly increased the exposure of sensitive OATP1B1/1B3 substrates. The 3D regimen drugs are UGT1A1 inhibitors and are predicted to moderately increase plasma exposure of sensitive UGT1A1 substrates. Paritaprevir, ritonavir, and dasabuvir are BCRP inhibitors. Victim DDI predictions were qualitatively in line with the clinical observations. Plasma exposures of the 3D regimen were reduced by strong CYP3A inducers (paritaprevir and ritonavir; major CYP3A substrates) but were not affected by strong CYP3A4 inhibitors, since ritonavir (a CYP3A inhibitor) is already present in the regimen. Strong CYP2C8 inhibitors increased plasma exposure of dasabuvir (a major CYP2C8 substrate), OATP1B1/1B3 inhibitors increased plasma exposure of paritaprevir (an OATP1B1/1B3 substrate), and P-gp or BCRP inhibitors (all compounds are substrates of P-gp and/or BCRP) increased plasma exposure of the 3D regimen. Overall, the comprehensive mechanistic assessment of compound disposition along with mechanistic and PBPK approaches to predict victim and perpetrator DDI liability may enable better clinical management of nonstudied drug combinations with the 3D regimen.

Preclinical pharmacokinetic/pharmacodynamic modeling and simulation in the pharmaceutical industry: an IQ consortium survey examining the current landscape.

The application of modeling and simulation techniques is increasingly common in preclinical stages of the drug discovery and development process. A survey focusing on preclinical pharmacokinetic/pharmacodynamics (PK/PD) analysis was conducted across pharmaceutical companies that are members of the International Consortium for Quality and Innovation in Pharmaceutical Development. Based on survey responses, ~68% of companies use preclinical PK/PD analysis in all therapeutic areas indicating its broad application. An important goal of preclinical PK/PD analysis in all pharmaceutical companies is for the selection/optimization of doses and/or dose regimens, including prediction of human efficacious doses. Oncology was the therapeutic area with the most PK/PD analysis support and where it showed the most impact. Consistent use of more complex systems pharmacology models and hybrid physiologically based pharmacokinetic models with PK/PD components was less common compared to traditional PK/PD models. Preclinical PK/PD analysis is increasingly being included in regulatory submissions with ~73% of companies including these data to some degree. Most companies (~86%) have seen impact of preclinical PK/PD analyses in drug development. Finally, ~59% of pharmaceutical companies have plans to expand their PK/PD modeling groups over the next 2 years indicating continued growth. The growth of preclinical PK/PD modeling groups in pharmaceutical industry is necessary to establish required resources and skills to further expand use of preclinical PK/PD modeling in a meaningful and impactful manner.

5-sulfonyl-benzimidazoles as selective CB2 agonists-part 2.

In a previous communication, the SAR of a series of potent and selective 5-sulfonyl-benzimidazole CB2-receptor agonists was described. The lack of in vivo activity of compounds from this series was attributed to their poor solubility and metabolic stability. In this Letter, we report on the further optimization of this series, leading to the relatively polar and peripherically acting CB2 agonists 41 and 49. Although both compounds were not active in acute pain models, the less selective compound 41 displayed good, sustained activity in a chronic model of neuropathic pain without the tolerance observed with morphine. In addition, both 41 and 49 delayed the onset of clinical symptoms in an experimental model for Multiple sclerosis.

Towards a better prediction of peak concentration, volume of distribution and half-life after oral drug administration in man, using allometry.

BACKGROUND: It is imperative that new drugs demonstrate adequate pharmacokinetic properties, allowing an optimal safety margin and convenient dosing regimens in clinical practice, which then lead to better patient compliance. Such pharmacokinetic properties include suitable peak (maximum) plasma drug concentration (C(max)), area under the plasma concentration-time curve (AUC) and a suitable half-life (t(½)). The C(max) and t(½) following oral drug administration are functions of the oral clearance (CL/F) and apparent volume of distribution during the terminal phase by the oral route (V(z)/F), each of which may be predicted and combined to estimate C(max) and t(½). Allometric scaling is a widely used methodology in the pharmaceutical industry to predict human pharmacokinetic parameters such as clearance and volume of distribution. In our previous published work, we have evaluated the use of allometry for prediction of CL/F and AUC. In this paper we describe the evaluation of different allometric scaling approaches for the prediction of C(max), V(z)/F and t(½) after oral drug administration in man. METHODS: Twenty-nine compounds developed at Janssen Research and Development (a division of Janssen Pharmaceutica NV), covering a wide range of physicochemical and pharmacokinetic properties, were selected. The C(max) following oral dosing of a compound was predicted using (i) simple allometry alone; (ii) simple allometry along with correction factors such as plasma protein binding (PPB), maximum life-span potential or brain weight (reverse rule of exponents, unbound C(max) approach); and (iii) an indirect approach using allometrically predicted CL/F and V(z)/F and absorption rate constant (k(a)). The k(a) was estimated from (i) in vivo pharmacokinetic experiments in preclinical species; and (ii) predicted effective permeability in man (P(eff)), using a Caco-2 permeability assay. The V(z)/F was predicted using allometric scaling with or without PPB correction. The t(½) was estimated from the allometrically predicted parameters CL/F and V(z)/F. Predictions were deemed adequate when errors were within a 2-fold range. RESULTS: C(max) and t(½) could be predicted within a 2-fold error range for 59% and 66% of the tested compounds, respectively, using allometrically predicted CL/F and V(z)/F. The best predictions for C(max) were obtained when k(a) values were calculated from the Caco-2 permeability assay. The V(z)/F was predicted within a 2-fold error range for 72% of compounds when PPB correction was applied as the correction factor for scaling. CONCLUSIONS: We conclude that (i) C(max) and t(½) are best predicted by indirect scaling approaches (using allometrically predicted CL/F and V(z)/F and accounting for k(a) derived from permeability assay); and (ii) the PPB is an important correction factor for the prediction of V(z)/F by using allometric scaling. Furthermore, additional work is warranted to understand the mechanisms governing the processes underlying determination of C(max) so that the empirical approaches can be fine-tuned further.

Cardio-vascular safety beyond hERG: in silico modelling of a guinea pig right atrium assay.

As chemists can easily produce large numbers of new potential drug candidates, there is growing demand for high capacity models that can help in driving the chemistry towards efficacious and safe candidates before progressing towards more complex models. Traditionally, the cardiovascular (CV) safety domain plays an important role in this process, as many preclinical CV biomarkers seem to have high prognostic value for the clinical outcome. Throughout the industry, traditional ion channel binding data are generated to drive the early selection process. Although this assay can generate data at high capacity, it has the disadvantage of producing high numbers of false negatives. Therefore, our company applies the isolated guinea pig right atrium (GPRA) assay early-on in discovery. This functional multi-channel/multi-receptor model seems much more predictive in identifying potential CV liabilities. Unfortunately however, its capacity is limited, and there is no room for full automation. We assessed the correlation between ion channel binding and the GPRA's Rate of Contraction (RC), Contractile Force (CF), and effective refractory frequency (ERF) measures assay using over six thousand different data points. Furthermore, the existing experimental knowledge base was used to develop a set of in silico classification models attempting to mimic the GPRA inhibitory activity. The Naïve Bayesian classifier was used to built several models, using the ion channel binding data or in silico computed properties and structural fingerprints as descriptors. The models were validated on an independent and diverse test set of 200 reference compounds. Performances were assessed on the bases of their overall accuracy, sensitivity and specificity in detecting both active and inactive molecules. Our data show that all in silico models are highly predictive of actual GPRA data, at a level equivalent or superior to the ion channel binding assays. Furthermore, the models were interpreted in terms of the descriptors used to highlight the undesirable areas in the explored chemical space, specifically regions of low polarity, high lipophilicity and high molecular weight. In conclusion, we developed a predictive in silico model of a complex physiological assay based on a large and high quality set of experimental data. This model allows high throughput in silico safety screening based on chemical structure within a given chemical space.

5-Sulfonyl-benzimidazoles as selective CB2 agonists.

A novel series of benzimidazole CB2-receptor agonists was synthesized and the structure-activity relationship explored. The results showed agonistic activities with an EC(50) up to 0.5 nM and excellent selectivity (>4000-fold) over the CB1 receptor. The size of the substituent on the 2-position determined the level of agonism, ranging from inverse agonism to partial agonism to full agonism, which was more pronounced for the rat CB2 receptor. A wide variation of sulfonyl substituents at the benzimidazole 5-position was tolerated, which was used to optimize the drug-like properties. This resulted into lead compound 14j that can be used to investigate the potential of a selective, peripherically acting CB2 agonist. The in vitro profile of key compounds is displayed using pie bar charts (VlaaiVis).

Predicting oral clearance in humans: how close can we get with allometry?

BACKGROUND: Oral clearance (CL/F) is an important pharmacokinetic parameter and plays an important role in the selection of a safe and tolerable dose for first-in-human studies. Throughout the pharmaceutical industry, many drugs are administered via the oral route; however, there are only a handful of published scaling studies for the prediction of oral pharmacokinetic parameters. METHODS: We evaluated the predictive performances of four different allometric approaches -- simple allometry (SA), the rule of exponents, the unbound CL/F approach, and the unbound fraction corrected intercept method (FCIM) -- for the prediction of human CL/F and the oral area under the plasma concentration-time curve (AUC). Twenty-four compounds developed at Johnson and Johnson Pharmaceutical Research and Development, covering a wide range of physicochemical and pharmacokinetic properties, were selected. The CL/F was predicted using these approaches, and the oral AUC was then estimated using the predicted CL/F. RESULTS: The results of this study indicated that the most successful predictions of CL/F and the oral AUC were obtained using the unbound CL/F approach in combination with the maximum lifespan potential or the brain weight as correction factors based on the rule of exponents. We also observed that the unbound CL/F approach gave better predictions when the exponent of SA was between 0.5 and 1.2. However, the FCIM seemed to be the method of choice when the exponent of SA was <0.50 or >1.2. CONCLUSIONS: Overall, we were able to predict CL/F and the oral AUC within 2-fold of the observed value for 79% and 83% of the compounds, respectively, by selecting the allometric approaches based on the exponents of SA.

Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs.

The aim of this study was to evaluate different physiologically based modeling strategies for the prediction of human pharmacokinetics. Plasma profiles after intravenous and oral dosing were simulated for 26 clinically tested drugs. Two mechanism-based predictions of human tissue-to-plasma partitioning (P(tp)) from physicochemical input (method Vd1) were evaluated for their ability to describe human volume of distribution at steady state (V(ss)). This method was compared with a strategy that combined predicted and experimentally determined in vivo rat P(tp) data (method Vd2). Best V(ss) predictions were obtained using method Vd2, providing that rat P(tp) input was corrected for interspecies differences in plasma protein binding (84% within 2-fold). V(ss) predictions from physicochemical input alone were poor (32% within 2-fold). Total body clearance (CL) was predicted as the sum of scaled rat renal clearance and hepatic clearance projected from in vitro metabolism data. Best CL predictions were obtained by disregarding both blood and microsomal or hepatocyte binding (method CL2, 74% within 2-fold), whereas strong bias was seen using both blood and microsomal or hepatocyte binding (method CL1, 53% within 2-fold). The physiologically based pharmacokinetics (PBPK) model, which combined methods Vd2 and CL2 yielded the most accurate predictions of in vivo terminal half-life (69% within 2-fold). The Gastroplus advanced compartmental absorption and transit model was used to construct an absorption-disposition model and provided accurate predictions of area under the plasma concentration-time profile, oral apparent volume of distribution, and maximum plasma concentration after oral dosing, with 74%, 70%, and 65% within 2-fold, respectively. This evaluation demonstrates that PBPK models can lead to reasonable predictions of human pharmacokinetics.

The prediction of drug metabolism, tissue distribution, and bioavailability of 50 structurally diverse compounds in rat using mechanism-based absorption, distribution, and metabolism prediction tools.

The aim of this study was to assess a physiologically based modeling approach for predicting drug metabolism, tissue distribution, and bioavailability in rat for a structurally diverse set of neutral and moderate-to-strong basic compounds (n = 50). Hepatic blood clearance (CL(h)) was projected using microsomal data and shown to be well predicted, irrespective of the type of hepatic extraction model (80% within 2-fold). Best predictions of CL(h) were obtained disregarding both plasma and microsomal protein binding, whereas strong bias was seen using either blood binding only or both plasma and microsomal protein binding. Two mechanistic tissue composition-based equations were evaluated for predicting volume of distribution (V(dss)) and tissue-to-plasma partitioning (P(tp)). A first approach, which accounted for ionic interactions with acidic phospholipids, resulted in accurate predictions of V(dss) (80% within 2-fold). In contrast, a second approach, which disregarded ionic interactions, was a poor predictor of V(dss) (60% within 2-fold). The first approach also yielded accurate predictions of P(tp) in muscle, heart, and kidney (80% within 3-fold), whereas in lung, liver, and brain, predictions ranged from 47% to 62% within 3-fold. Using the second approach, P(tp) prediction accuracy in muscle, heart, and kidney was on average 70% within 3-fold, and ranged from 24% to 54% in all other tissues. Combining all methods for predicting V(dss) and CL(h) resulted in accurate predictions of the in vivo half-life (70% within 2-fold). Oral bioavailability was well predicted using CL(h) data and Gastroplus Software (80% within 2-fold). These results illustrate that physiologically based prediction tools can provide accurate predictions of rat pharmacokinetics.