New Chemical Entities Research Articles

Deep generative models for ligand-based de novo design applied to multi-parametric optimization.

Multi-parameter optimization (MPO) is a major challenge in new chemical entity (NCE) drug discovery. Recently, promising results were reported for deep learning generative models applied to de novo molecular design, but, to our knowledge, until now no report was made of the value of this new technology for addressing MPO in an actual drug discovery project. In this study, we demonstrate the benefit of applying AI technology in a real drug discovery project. We evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the obtention of lead compounds meeting 11 different biological activity objectives simultaneously. Using the initial dataset of the project, we built QSAR models for all the 11 objectives, with moderate to high performance (precision between 0.67 and 1.0 on an independent test set). Our DL-based AI de novo design algorithm, combined with the QSAR models, generated 150 virtual compounds predicted as active on all objectives. Eleven were synthetized and tested. The AI-designed compounds met 9.5 objectives on average (i.e., 86% success rate) versus 6.4 (i.e., 58% success rate) for the initial molecules measured on all objectives. One of the AI-designed molecules was active on all 11 measured objectives, and two were active on 10 objectives while being in the error margin of the assay for the last one. The AI algorithm designed compounds with functional groups, which, although being rare or absent in the initial dataset, turned out to be highly beneficial for the MPO.

PET as a Translational Tool in Drug Development for Neuroscience Compounds.

In central nervous system drug discovery programs, early development of new chemical entities (NCEs) requires a multidisciplinary strategy and a translational approach to obtain proof of distribution, proof of occupancy, and proof of function in specific brain circuits. Positron emission tomography (PET) provides a way to assess in vivo the brain distribution of NCEs and their binding to the target of interest, provided that radiolabeling of the NCE is possible or that a suitable radioligand is available. PET is therefore a key tool for early phases of drug discovery programs. This review will summarize the main applications of PET in early drug development and discuss the usefulness of PET microdosing studies performed with direct labelling of the NCE and PET occupancy studies. The purpose of this review is also to propose an alignment of the nomenclatures used by drug metabolism and pharmacokinetic scientists and PET imaging scientists to indicate key pharmacokinetic parameters and to provide guidance in the performance and interpretation of PET studies.

State of the Art and Uses for the Biopharmaceutics Drug Disposition Classification System (BDDCS): New Additions, Revisions, and Citation References

The Biopharmaceutics Drug Disposition Classification system (BDDCS) is a four-class approach based on water solubility and extent of metabolism/permeability rate. Based on the BDDCS class to which a drug is assigned, it is possible to predict the role of metabolic enzymes and transporters on the drug disposition of a new molecular entity (NME) prior to its administration to animals or humans. Here, we report a total of 1475 drugs and active metabolites to which the BDDCS is applied. Of these, 379 are new entries, and 1096 are revisions of former classification studies with the addition of references for the approved maximum dose strength, extent of the systemically available drug excreted unchanged in the urine, and lowest solubility over the pH range 1.0–6.8 when such information is available in the literature. We detail revised class assignments of previously misclassified drugs and the literature analyses to classify new drugs. We review the process of solubility assessment for NMEs prior to drug dosing in humans and approved dose classification, as well as the comparison of Biopharmaceutics Classification System (BCS) versus BDDCS assignment. We detail the uses of BDDCS in predicting, prior to dosing animals or humans, disposition characteristics, potential brain penetration, food effect, and drug-induced liver injury (DILI) potential. This work provides an update on the current status of the BDDCS and its uses in the drug development process.Graphical abstract

The impact of science-intensive drugs on longevity and cure rate: Evidence from new prescription drugs launched in Japan

This paper examines how new science-intensive drugs improve the longevity of the population and the cure rate of hospitalized patients (inpatients). We develop a comprehensive longitudinal-disease-level panel data set, matched with drugs with a new molecular entity (NME) launched in Japan and classified by science intensity. Estimates suggest that the increase in the diversity of science-intensive NMEs (or NME stock) used for treating a disease significantly contributed to longevity and to the improvement of the cure rate of inpatients. In contrast, we find that non science-intensive NME stock does not significantly enhance these outcomes. Our results also suggest that controlling for the effects of surgeries does not reduce the effects of science-intensive drugs and that quality of science exploited for the discovery of an NME also plays a role in the effects. We have validated our indicator of science-intensive NMEs as a measure of the contribution of drugs to treatment, based on a survey of physicians. The results show that scientific advancement and exploitation for drug innovations critically contribute to improving human welfare.

Predicting Solubility of Newly-Approved Drugs (2016-2020) with a Simple ABSOLV and GSE(Flexible-Acceptor) Consensus Model Outperforming Random Forest Regression.

This study applies the ‘Flexible-Acceptor’ variant of the General Solubility Equation, GSE(Φ,B), to the prediction of the aqueous intrinsic solubility, log10 S0, of FDA recently-approved (2016–2020) ‘small-molecule’ new molecular entities (NMEs). The novel equation had been shown to predict the solubility of drugs beyond Lipinski’s ‘Rule of 5’ chemical space (bRo5) to a precision nearly matching that of the Random Forest Regression (RFR) machine learning method. Since then, it was found that the GSE(Φ,B) appears to work well not only for bRo5 NMEs, but also for Ro5 drugs. To put context to GSE(Φ,B), Yalkowsky’s GSE(classic), Abraham’s ABSOLV, and Breiman’s RFR models were also applied to predict log10 S0 of 72 newly-approve NMEs, for which useable reported solubility values could be accessed (nearly 60% from FDA New Drug Application published reports). Except for GSE (classic), the prediction models were retrained with an enlarged version of the Wiki-pS0 database (nearly 400 added log10 S0 entries since our recent previous study). Thus, these four models were further validated by the additional independent solubility measurements which the newly-approved drugs introduced. The prediction methods ranked RFR ~ GSE (Φ,B) > ABSOLV > GSE (classic) in performance. It was further demonstrated that the biases generated in the four separate models could be nearly eliminated in a consensus model based on the average of just two of the methods: GSE (Φ,B) and ABSOLV. The resulting consensus prediction equation is simple in form and can be easily incorporated into spreadsheet calculations. Even more significant, it slightly outperformed the RFR method.

Sources of Evidence Triggering and Supporting Safety-Related Labeling Changes: A 10-Year Longitudinal Assessment of 22 New Molecular Entities Approved in 2008 by the US Food and Drug Administration.

New safety issues concerning US FDA-approved drugs are commonly communicated through safety-related labeling changes. Therefore, to optimize and refine postmarket safety surveillance strategies, it is important to comprehensively characterize the sources of data giving rise to safety-related labeling changes. Our objective was to characterize the sources of data triggering and supporting the identification of new safety risks of FDA-approved drugs communicated through safety-related labeling changes. We conducted a retrospective study with a 10-year observation period using FDA's internal electronic data repositories for all prescription new molecular entities (NME) approved in 2008. We collected and analyzed information on new safety issues, the section of the full prescribing information updated, initiators (FDA, drug manufacturer), and triggering and supporting sources of evidence. Among 22 NMEs approved in 2008, 189 new safety issues for 18 NMEs were identified.Compared to drug manufacturer, FDA initiated safety-related labeling changes in nine of the ten changes to theBoxed Warnings, 28 of the 52 changes to the Warnings and Precautions, and 43 of the 134 changes to the Adverse Reactions sections of the full prescribing information. The most frequent triggering sources of evidence included the drug manufacturer safety database (32.3%) and FDA Adverse Event Reporting System (FAERS) safety reports (15.3%) for all relevant sections of the full prescribing information, and class-labeling changes (17.5%) for Boxed Warnings and the Warnings and Precautions sections. The most frequent triggering source of evidence was FAERS safety reports (69%) in the first year after drug approval and the drug manufacturer safety database in subsequent years. Our findings emphasize the continued importance of safety reports from FAERS and drug manufacturer safety databases and a comprehensive drug safety surveillance program throughout a drug's lifecycle.

Assessment of Abrasion-Induced Visual Defects in Twin Screw Wet Granulation Using Wall Friction Measurements

Starting point of the presented study were abrasion effects occurring during a twin screw wet granulation (TSG) process of a new chemical entity (NCE) formulation, resulting in gray spots on the final tablets. Several actions and systematic changes of equipment and process parameter settings of TSG process were conducted which reduced the visual defect rate of the tablets, i.e., gray spots on the surface, below the specification limit. To understand the rationale and mechanism behind these improvements, correlations of defect rates and wall friction measurements using a Schulze ring shear tester were evaluated. To check the suitability of the method, a broad range of wall materials as well as powder formulations at various moisture levels were investigated with regard to their wall friction angle. As differences in wall friction angle could be detected, further experiments were conducted using wall material samples made out of different screw materials for TSG. Evaluation of these screw wall material samples gave first hints, which screw materials should be preferred in regard of friction for TSG process. In the finally presented case study, wall friction measurements were performed using the above mentioned NCE formulation with known abrasion issues at TSG processing. The results confirmed that changes which led to a reduced visual defect rate of tablets correlated with a decreased wall friction angle. The results suggest wall friction measurements as a potent tool for equipment selection and establishment of a suitable process window prior to conducting TSG experiments.Graphical abstract

Application of Solubility and Dissolution Profile Comparison for Prediction of Gastric pH-Mediated Drug-Drug Interactions.

The objective of this study was to assess how solubility and dissolution profile comparisons under different pH conditions can be used to predict gastric pH-mediated drug-drug interaction (DDI) potential. We collected information for new molecular entities (NMEs) approved from 2003 to 2019 by the U.S. Food and Drug Administration (FDA) that had dedicated clinical DDI studies with acid-reducing agents (ARAs). Among these, 67 NMEs with solubility under different pHs and dissolution profiles generated in pH 1.2, 4.5, and 6.8 aqueous media were included for analysis. Similarity factor (f2) was used to compare dissolution profiles at different pHs for pH-mediated DDI prediction (e.g., f2<50 predicts positive DDI). Prediction accuracy was calculated based on the outcome comparison between predicted and observed DDIs. Based on dissolution profile comparisons and observed DDI data, weak base drugs (WBDs) (n = 49) showed 72.5% prediction accuracy under the fasted conditions, and 66.7% prediction accuracy under fed conditions. While using solubility and clinical dose for prediction, the prediction accuracy was 80% under fasted conditions and 66.7% under fed conditions, respectively. Comparison of dissolution profiles generated at pH 1.2, 4.5, and 6.8 can be used to predict gastric pH-mediated DDI potential for WBDs. It demonstrated comparable prediction accuracy under both fasted and fed conditions when compared to the prediction using solubility and clinical dose. Furthermore, dissolution profile comparison could add an additional understanding of possible impact of pH change on the release behavior of the drug product. Graphical abstract.

Ethnobotanical Review of Medicinal Plants Used Against Diarrhea and Dysentery in Northeast India (Assam)

Background: Since primitive times, plants have been extensively utilized in conventional remedies for primary health care. It is observed that medicinal plants have various bioactive components. It becomes an alternative choice for synthetic medications to treat diarrhea and dysentery, which are the primary waterborne diseases with high mortality rates that bring substantial health threats to global populations. Objective: The present review aims to look over the ethnobotanical knowledge for the treatment of diarrhea and dysentery and folklore practices by the people prevailing in Assam. Methods: In this perspective, an extensive literature survey was carried out to understand the mechanism, control, and treatment of diarrhea and dysentery in different online academic databases and books. An advanced search was carried out in 'PubMed' and 'Google Scholar' using the term "Phytoconstituents" and "antidiarrheal" along with "Phytoconstituents" and "anti-amoebic". Results: Data retrieved from databases were analyzed and interpreted to conclude that in Assam, diarrhea and dysentery are the primary leading causes of mortality among children under five years. It is mainly due to the unhygienic livelihood, unavailability of safe drinking water, unhealthy food, seasonal rainfall, flood, and open defecation. The present investigations reveal that the people of Assam use 39 plant species belonging to 36 families to cure diarrhea and dysentery. Conclusion: The present study established the effective use of medicinal plants by various communities in Assam to treat diarrhea and dysentery. Furthermore, it can be used to develop a new therapeutic approach to create new chemical entities (NCE) in drug discovery, which are safe, fruitful, and inexpensive.

Radioiodine Labeling Reagents and Methods for New Chemical Entities and Biomolecules.

Several radioisotopes of iodine (123I, 124I, 125I, and 131I) are available for medical use. One of them can be used, depending on the application, for radioiodine labeling of New Chemical Entities (NCEs) and biomolecules (peptides, proteins, protein fragments, monoclonal antibodies, etc.) for the development of novel imaging and therapeutic pharmaceuticals. Direct, using inorganic and organic oxidizing agents and enzyme catalysts, and indirect, using prosthetic groups, radioiodine-labeling methods have been used routinely in the past. In this report, a comprehensive review of the physical properties of various iodine radionuclides, their medical applications, and a summary of various radioiodine labeling reagents and methods for NCEs and biomolecules are provided.

FDA Oncology Center of Excellence Review of Real World Data Submissions Supporting Drug Development in Hematologic Malignancies

BackgroundAligning with 21st Century Cures legislation, FDA is exploring methodologies to advance appropriate uses of Real World Data (RWD) to generate Real World Evidence (RWE). RWD to support regulatory decision making has markedly increased in oncology. This review specifically focused on the analysis of RWD containing submissions for medical products in development for the treatment of hematological malignancies and associated treatment related conditions (e.g., Cytokine Release Syndrome (CRS), Graft Versus Host Disease (GVHD).MethodsA systematic search was conducted using internal FDA databases to identify RWD submissions from 2010 to 2020. Search terms included: real world evidence, real world data, electronic health record, cancer registry, administrative claims, external control arm, observational cohort, historical control arm, real world Overall Survival (rwOS) , real world Response Rate (rwRR), real world Overall Response Rate rwORR and real world Complete Response (rwCR). Regulatory submissions specific to malignant hematology and associated treatment related conditions were reviewed, and pre-defined common data elements were extracted and validated by independent dual review. Descriptive statistics were calculated.ResultsA total of 142 regulatory submissions included RWD from 2011-2020. A subset of 94 RWD submissions met the criteria for further evaluation, of which 20 (21%) submissions corresponding with 14 molecular entities were for hematologic malignancies or treatment related conditions (e.g., CRS, GVHD). RWD submissions increased substantially over time, with 14 (70%) of submissions received between 2019-2020. Specific evaluation for pediatric indications was referenced in 15% of submissions. The most commonly referenced RWD source was EHR data (55%), followed by use of multiple sources (20%), and registry data (15%). Approximately 90% of the submissions aimed to support treatment effectiveness. Primary RWD study objectives included supporting approval of a new molecular entity (NME) (40%), expanding an approved indication (25%), conversion from accelerated to regular approval (15%), and providing data to inform postmarketing safety evaluation (20%). Among RWD submissions, response endpoints (e.g., rwORR, rwCR, rwPR, Partial Response) and overall survival (e.g., rwOS) were most frequently selected as primary outcomes for 50% and 20% of proposals respectively; however, these outcomes were included as any endpoint in 65% and 75% of submissions.ConclusionThis review demonstrates increasing use of various RWD sources to support evidence generation for drug development in hematologic malignancies and associated treatment related conditions with the primary objective of supporting demonstration of effectiveness using rwOS or real world response measures as primary endpoints. Given the increased inclusion of RWD in regulatory submissions, further methodological development is needed, including in the selection and validation of rwEndpoints. Appropriate study design must be aligned with a clear regulatory objective to ensure that RWD can be adequately evaluated. Additionally, the development of standardized metrics for data characterization and transparency in reporting of RWD are foundational steps to the evaluation of fit for purpose RWD to support regulatory decision making. DisclosuresNo relevant conflicts of interest to declare.

Challenges with sex-specific subgroup analyses in oncology clinical trials for drug approvals between 2015–2020

IntroductionWomen continue to be underrepresented in oncology clinical trials, leading to poor, underpowered subgroup analyses that cannot be generalized to cancer patients in practice. In 2014, the US Food and Drug Administration (FDA) released an Action Plan, which included actions to improve the quality and reporting of demographic subgroup data. We sought to evaluate the five-year progress since the release of this report by assessing the credibility of sex-specific subgroup analyses in oncology clinical trials. MethodsWe reviewed the FDA Hematology/Oncology Approvals website for New Molecular Entities (NMEs) that were approved for adults from 2015 to 2020. Publications and their supplementary indexes were reviewed by two authors (K.J. & A.R.) against ten criteria that gauge the credibility of subgroup analyses by assessing factors related to study design, analysis, and context. One point was awarded for each criteria met, for a maximum score of 10. ResultsWe identified a total of 73 NMEs approved for cancer treatment between 2015–2020, of which 61 met our eligibility criteria. Of these, 32 studies (52 %) reported a subgroup analysis by sex and were included in our analysis. Phase 2 (41 %) and Phase 3 (53 %) studies represented most studies. No study met ≥3 credibility criteria. ConclusionOnly half the studies included in our analysis reported outcomes by sex, which suggests the activities stipulated in the 2014 US FDA Action Plan might be ineffective. This is concerning as uncredible sex-specific subgroup analyses can lead to wrongful clinical decision-making and poor patient outcomes. Policy summaryOur findings suggest sex-specific subgroup analyses in oncology are not credible and users of these data should interpret results with caution. Regulatory bodies, such as the US FDA, ought to mandate subgroup analyses by demographic groups in drug applications. Peer-reviewed journals could ensure investigators disclose study results by sex as a condition for publication.

Racial and Ethnic Differences in Drug Disposition and Response: Review of New Molecular Entities Approved Between 2014 and 2019.

Race and ethnicity can contribute to differences in drug exposure and/or response. Here, we report that about 10% of the new molecular entities (NMEs) approved between 2014 and 2019 by the US Food and Drug Administration's Center for Drug Evaluation and Research showed differences in exposure and/or response based on race/ethnicity or pharmacogenetic factors known to vary in frequency across global populations. Fewer NMEs (10%) reported differences in the labeling in 2014 to 2019 when compared to about 21% of NMEs approved between 2008 and 2013 that had differences in pharmacokinetics, safety, response, and/or pharmacogenetics. Understanding the underlying mechanisms that lead to such differences and adequate enrollment of racial and ethnic subgroups is essential to obtain sufficient information on exposure and response. Though drug development is global, when heterogeneous populations are not adequately enrolled, the risk-benefit assessments can remain incomplete for certain subgroups. Consequently, this can result in regional differences in drug approval, population-specific prescribing recommendations, or need for additional postmarketing studies to address concerns related to exposure, response, or lack of representation that lead to gaps in information.

Differential diffusion of pharmaceutical innovations in a mixed market middle - income economy

BackgroundPolicymakers are faced with the challenge of balancing patient’s access for effective and affordable medicines to sustain the rising healthcare costs. In a mixed healthcare market such as Malaysia, coverage decisions of new medicines are different: public funded health system has a formulary listing process whereas for private sector, which is a market-based economy, depends on patient’s willingness to pay and insurance coverage. There is little overlap between public and private healthcare service delivery with access to new innovative medicines, as differentiated by sources of funding. The objectives of this study were to examine the diffusion of New Chemical Entities (NCEs) into the public and private healthcare market between 2010 and 2014, and determine the factors explaining the diffusion.MethodsWe matched medicines from the product registration database by medicine formulation to medicines in IQVIA National Pharmaceutical Audit database for each year. The price per Defined Daily Dose (DDD), market concentration and generic utilization share variables were calculated. A panel fixed effect model was performed to measure diffusion of NCEs for each year and test possible determinants of diffusion of NCEs for overall market and sector specifics.ResultsThe utilization of NCEs was larger in the private sector compared to the public sector but the speed of diffusion over time was higher in the public sector. Price per DDD was negatively associated with diffusion of NCEs, while generic utilization share was significantly regressive in the public sector. Market concentration was negatively associated with utilization of NCEs, however result tends to be mixed according to sector and Anatomical Therapeutic Chemical (ATC) category.ConclusionsUnderstanding key aspects of sectoral variation in diffusion of NCEs are crucial to reduce the differences of access to new medicines within a country and ensure resources are used on cost effective treatments.

Novel candidates in the clinical development pipeline for TB drug development and their synthetic approaches.

Tuberculosis (TB) is an infection caused by Mycobacterium tuberculosis (Mtb) and one of the deadliest infectious diseases in the world. Mtb has the ability to become dormant within the host and to develop resistance. Hence, new antitubercular agents are required to overcome problems in the treatment of multi-drug-resistant Tb (MDR-Tb) and extensively drug-resistant Tb (XDR-Tb) along with shortening the treatment time. Several efforts are being made to develop very effective new drugs for Tb, within the pharmaceutical industry, the academia and through public-private partnerships. This review will address the antitubercular activities, biological target, mode of action, synthetic approaches and thoughtful concept for the development of several new drugs currently in the clinical trial pipeline (up to October 2019) for tuberculosis. The aim of this review may be very useful in scheming new chemical entities (NCEs) for Mtb.

Theoretical Studies on the Molecular Properties, Toxicity, and Biological Efficacy of 21 New Chemical Entities.

New chemical entities (NCEs) such as small molecules and antibody–drug conjugates have strong binding affinity for biological targets, which provide deep insights into structure-specific interactions for the design of future drugs. As structures of drugs increase in complexity, the importance of computational predictions comes into sharp focus. Knowledge of various computational tools enables us to predict the molecular properties, toxicity, and biological efficacy of the drugs and help the medicinal chemists to discover new drugs more efficiently. Newly approved drugs have higher affinities for proteins and nucleic acids and are applied for the treatment of human diseases. We have carried out the computational studies of 21 such NCEs, specifically small molecules and antibody–drug conjugates, and studied the biological efficacy of these drugs. Their bioactivity score and molecular and pharmacokinetic properties were evaluated using online computer software programs, viz., Molinspiration and Osiris Property Explorer. The SwissTargetPrediction tool was used for the efficient prediction of protein targets for the NCEs. The results indicated higher stability for the drug complexes due to a larger HOMO–LUMO gap. A high electrophilicity index reflects good electrophilic behavior and high reactivity of the drugs. Lipinski’s ‘‘rule of five’’ indicated that most of the drug complexes are likely to be orally active. These drugs also showed non-mutagenic, non-tumorigenic, non-irritant, and non-effective reproductive behavior. We hope that these studies will provide an insight into molecular recognition and definitely help the medicinal chemists to design new drugs in future.

Racial representation in clinical trials for dermatological new molecular entities.

Under-representation of ethnic minorities in clinical research has major implications for equality of access to current treatments in the field of dermatology. To determine whether there has been equitable representation of black individuals in the clinical trials for dermatological new molecular entities (NMEs) approved by the US Food and Drug Administration (FDA) since 2015, we analysed data from the FDA Drug Trials Snapshots programme from January 2015 to the present. During this period, there was significant under-representation of black participants in clinical trials for NMEs treating acne vulgaris, plaque psoriasis, actinic keratosis and squamous cell carcinoma. These findings highlight the need to prioritize representation of ethnic minorities in clinical trials to enhance clinical practice in the field of dermatology and to improve the care and health of minorities.

Species selection for nonclinical safety assessment of drug candidates: Examples of current industry practice

In drug development, nonclinical safety assessment is pivotal for human risk assessment and support of clinical development. Selecting the relevant/appropriate animal species for toxicity testing increases the likelihood of detecting potential effects in humans, and although recent regulatory guidelines state the need to justify or dis-qualify animal species for toxicity testing, individual companies have developed decision-processes most appropriate for their molecules, experience and 3Rs policies. These generally revolve around similarity of metabolic profiles between toxicology species/humans and relevant pharmacological activity in at least one species for New Chemical Entities (NCEs), whilst for large molecules (biologics) the key aspect is similarity/presence of the intended human target epitope.To explore current industry practice, a questionnaire was developed to capture relevant information around process, documentation and tools/factors used for species selection. Collated results from 14 companies (Contract Research Organisations and pharmaceutical companies) are presented, along with some case-examples or over-riding principles from individual companies. As the process and justification of species selection is expected to be a topic for continued emphasis, this information could be adapted towards a harmonized approach or best practice for industry consideration.

SMINBR: An Integrated Network and Chemoinformatics Tool Specialized for Prediction of Two-Component Crystal Formation.

Two-component crystals such as pharmaceutical cocrystals and salts have been proven as an effective strategy to improve physicochemical and biopharmaceutical properties of drugs. It is not easy to select proper molecular combinations to form two-component crystals. The network-based models have been successfully utilized to guide cocrystal design. Yet, the traditional social network-derived methods based on molecular-interaction topology information cannot directly predict interaction partners for new chemical entities (NCEs) that have not been observed to form two-component crystals. Herein, we proposed an effective tool, namely substructure-molecular-interaction network-based recommendation (SMINBR), to prioritize potential interaction partners for NCEs. This in silico tool incorporates network and chemoinformatics methods to bridge the gap between NCEs and known molecular-interaction network. The high performance of 10-fold cross validation and external validation shows the high accuracy and good generalization capability of the model. As a case study, top 10 recommended coformers for apatinib were all experimentally confirmed and a new apatinib cocrystal with paradioxybenzene was obtained. The predictive capability of the model attributes to its accordance with complementary patterns driving the formation of intermolecular interactions. SMINBR could automatically recommend new interaction partners for a target molecule, and would be an effective tool to guide cocrystal design. A free web server for SMINBR is available at http://lmmd.ecust.edu.cn/sminbr/.

Drug Interactions Involving 17α-Ethinylestradiol: Considerations Beyond Cytochrome P450 3A Induction and Inhibition.

It is widely acknowledged that drug-drug interactions (DDIs) involving estrogen (17α-ethinylestradiol (EE))-containing oral contraceptives (OCs) are important. Consequently, sponsors of new molecular entities (NMEs) often conduct clinical studies with priority given to OCs as victims of cytochrome P450 (CYP) 3A (CYP3A) induction and inhibition. Such scenarios are reflected in the US Food and Drug Administration-issued guidance documentation related to OC DDI studies. Although CYP3A is important, OCs such as EE are metabolized by sulfotransferase 1E1 and UDP-glucuronosyltransferase (UGT) 1A1, expressed in the gut and liver, and so both can also serve as loci of victim OC DDI. Therefore, for any NME, one should carefully consider its induction and inhibition profile involving CYP3A4/5, UGT1A1, and SULT1E1. As DDI perpetrators, available clinical DDI data indicate that EE-containing OCs can induce (e.g., UGT1A4 and CYP2A6) and inhibit (CYP1A2 ≥ CYP2C19 > CYP3A4/5 > CYP2C8, CYP2B6, CYP2D6, and CYP2C9) various CYP forms. Although available in vitro CYP inhibition data do not explain such a graded inhibitory effect in vivo, it is hypothesized that EE differentially modulates CYP expression via potent agonism of the estrogen receptor expressed in the gut and liver. From the standpoint of the NME as potential OC DDI victim, therefore, it is important to assess its projected (pre-phase I) or known therapeutic index and pharmacokinetic profile (fraction absorbed, absolute oral bioavailability, clearance/extraction class, fraction metabolized by CYP1A2, CYP2C19, CYP2A6, and UGT1A4). Such information can enable the prioritization, design, and interpretation of NME-OC DDI studies.