The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations.

Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms can drastically reduce drug development timelines and costs. While such efforts were initially focused primarily on target affinity/activity, it is now appreciated that other parameters are equally important in the successful development of a drug and its progression to the clinic, including pharmacokinetic properties as well as absorption, distribution, metabolic, excretion and toxicological (ADMET) properties. In the last decade, several programs have been developed that incorporate these properties into the drug design and optimization process and to varying degrees, allowing for multi-parameter optimization. Here, we introduce the Artificial Intelligence-driven Drug Design (AIDD) platform, which automates the drug design process by integrating high-throughput physiologically-based pharmacokinetic simulations (powered by GastroPlus) and ADMET predictions (powered by ADMET Predictor) with an advanced evolutionary algorithm that is quite different than current generative models. AIDD uses these and other estimates in iteratively performing multi-objective optimizations to produce novel molecules that are active and lead-like. Here we describe the AIDD workflow and details of the methodologies involved therein. We use a dataset of triazolopyrimidine inhibitors of the dihydroorotate dehydrogenase from Plasmodium falciparum to illustrate how AIDD generates novel sets of molecules.

A clinical practice review of percutaneous ethanol injection for thyroid nodules: state of the art for benign, cystic lesions.

Percutaneous ethanol injection (PEI) is a widely used treatment option for cystic and predominantly cystic thyroid nodules. It has several advantages over other treatment modalities. Compared to surgery, PEI is less painful, can be performed in the outpatient setting, and carries less risk of transient or permanent side effects. Compared to other minimally invasive techniques such as radiofrequency ablation (RFA), PEI is less expensive and does not require specialized equipment. PEI performs well in the context of cystic nodules. PEI does not perform as well as other techniques in solid nodules, so its use as a primary treatment is limited to cystic and predominantly cystic thyroid nodules. However, PEI is also being explored as an adjunct treatment to improve ablation of solid nodules with other techniques. Here, we provide a clinical review discussing the genesis, mechanism of action, and patient selection with respect to ethanol ablation, as well as the procedure itself. Predictors of operative success, failure, and common adverse events are also summarized. Altogether, PEI allows impressive volume reduction rates with minimal complications. Several recent studies have also evaluated the long-term impact of PEI up to 10 years after treatment and revealed maintenance of robust treatment efficacy with no undesirable long-term sequelae. Thus, PEI remains the treatment of choice for benign but symptomatic cystic and predominantly cystic thyroid nodules.

Precision-cut lung slices as an ex vivo model to study Pneumocystis murina survival and antimicrobial susceptibility.

IMPORTANCE: Our study reveals the potential of precision-cut lung slices as an ex vivo platform to study the growth/survival of Pneumocystis spp. that can facilitate the development of new anti-fungal drugs.

The Diagnostic Performance of Ultrasonography in the Evaluation of Extrathyroidal Extension in Papillary Thyroid Carcinoma: A Systematic Review and Meta-Analysis.

Extrathyroidal extension (ETE) in patients with papillary thyroid carcinoma (PTC) is an indication of disease progression and can influence treatment aggressiveness. This meta-analysis assesses the diagnostic accuracy of ultrasonography (US) in detecting ETE. A systematic review and meta-analysis were performed by searching PubMed, Embase, and Cochrane for studies published up to April 2022. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated. The areas under the curve (AUC) for summary receiver operating curves were compared. A total of 11 studies analyzed ETE in 3795 patients with PTC. The sensitivity of ETE detection was 76% (95%CI = 74-78%). The specificity of ETE detection was 51% (95%CI = 49-54%). The DOR of detecting ETE by US was 5.32 (95%CI = 2.54-11.14). The AUC of ETE detection was determined to be 0.6874 ± 0.0841. We report an up-to-date analysis elucidating the diagnostic accuracy of ETE detection by US. Our work suggests the diagnostic accuracy of US in detecting ETE is adequate. Considering the importance of ETE detection on preoperative assessment, ancillary studies such as adjunct imaging studies and genetic testing should be considered.

Optimization of AAV6 transduction enhances site-specific genome editing of primary human lymphocytes.

Adeno-associated virus serotype 6 (AAV6) is a valuable reagent for genome editing of hematopoietic cells due to its ability to serve as a homology donor template. However, a comprehensive study of AAV6 transduction of hematopoietic cells in culture, with the goal of maximizing ex vivo genome editing, has not been reported. Here, we evaluated how the presence of serum, culture volume, transduction time, and electroporation parameters could influence AAV6 transduction. Based on these results, we identified an optimized protocol for genome editing of human lymphocytes based on a short, highly concentrated AAV6 transduction in the absence of serum, followed by electroporation with a targeted nuclease. In human CD4+ T cells and B cells, this protocol improved editing rates up to 7-fold and 21-fold, respectively, when compared to standard AAV6 transduction protocols described in the literature. As a result, editing frequencies could be maintained using 50- to 100-fold less AAV6, which also reduced cellular toxicity. Our results highlight the important contribution of cell culture conditions for ex vivo genome editing with AAV6 vectors and provide a blueprint for improving AAV6-mediated homology-directed editing of human T and B cells.

Rational Selection of CRISPR-Cas9 Guide RNAs for Homology-Directed Genome Editing.

Homology-directed repair (HDR) of a DNA break allows copying of genetic material from an exogenous DNA template and is frequently exploited in CRISPR-Cas9 genome editing. However, HDR is in competition with other DNA repair pathways, including non-homologous end joining (NHEJ) and microhomology-mediated end joining (MMEJ), and the efficiency of HDR outcomes is not predictable. Consequently, to optimize HDR editing, panels of CRISPR-Cas9 guide RNAs (gRNAs) and matched homology templates must be evaluated. We report here that CRISPR-Cas9 indel signatures can instead be used to identify gRNAs that maximize HDR outcomes. Specifically, we show that the frequency of deletions resulting from MMEJ repair, characterized as deletions greater than or equal to 3 bp, better predicts HDR frequency than consideration of total indel frequency. We further demonstrate that tools that predict gRNA indel signatures can be repurposed to identify gRNAs to promote HDR. Finally, by comparing indels generated by S. aureus and S. pyogenes Cas9 targeted to the same site, we add to the growing body of data that the targeted DNA sequence is a major factor governing genome editing outcomes.

Design and tests of prospective property predictions for novel antimalarial 2-aminopropylaminoquinolones.

There is a pressing need to improve the efficiency of drug development, and nowhere is that need more clear than in the case of neglected diseases like malaria. The peculiarities of pyrimidine metabolism in Plasmodium species make inhibition of dihydroorotate dehydrogenase (DHODH) an attractive target for antimalarial drug design. By applying a pair of complementary quantitative structure-activity relationships derived for inhibition of a truncated, soluble form of the enzyme from Plasmodium falciparum (s-PfDHODH) to data from a large-scale phenotypic screen against cultured parasites, we were able to identify a class of antimalarial leads that inhibit the enzyme and abolish parasite growth in blood culture. Novel analogs extending that class were designed and synthesized with a goal of improving potency as well as the general pharmacokinetic and toxicological profiles. Their synthesis also represented an opportunity to prospectively validate our in silico property predictions. The seven analogs synthesized exhibited physicochemical properties in good agreement with prediction, and five of them were more active against P. falciparum growing in blood culture than any of the compounds in the published lead series. The particular analogs prepared did not inhibit s-PfDHODH in vitro, but advanced biological assays indicated that other examples from the class did inhibit intact PfDHODH bound to the mitochondrial membrane. The new analogs, however, killed the parasites by acting through some other, unidentified mechanism 24-48 h before PfDHODH inhibition would be expected to do so.

Phytocannabinoid drug-drug interactions and their clinical implications.

Cannabis is a plant with a long history of human pharmacological use, both for recreational purposes and as a medicinal remedy. Many potential modern medical applications for cannabis have been proposed and are currently under investigation. However, its rich chemical content implies many possible physiological actions. As the use of medicinal cannabis has gained significant attention over the past few years, it is very important to understand phytocannabinoid dispositions within the human body, and especially their metabolic pathways. Even though the complex metabolism of phytocannabinoids poses many challenges, a more thorough understanding generates many opportunities, especially regarding possible drug-drug interactions (DDIs). Within this context, computer simulations are most commonly used for predicting substrates and inhibitors of metabolic enzymes. These predictions can assist to identify metabolic pathways by understanding individual CYP isoform specificities to a given molecule, which can help to predict potential enzyme inhibitions and DDIs. The reported in vivo Phase I and Phase II metabolisms of various phytocannabinoids are herein reviewed, accompanied by a parallel in silico analysis of their predicted metabolism, highlighting the clinical importance of such understanding in terms of DDIs and clinical outcomes.

Putting deep learning in perspective for pest management scientists.

'Deep learning' is causing rapid technological changes in many fields of science, and conjectures about its potential for transforming everyone's work and lives is a matter of great debate. Unfortunately, it is all too easy to apply it as a 'black box' tool with little consideration of its potential limitations, especially when the data it is being applied to is less than perfect. In this Perspective, I try to put deep learning into a broader mechanistic and historical context by showing how it relates to older forms of artificial intelligence; by providing a general explanation of how it operates; and by exploring some of the challenges involved in its implementation. Examples wherein it has been applied to pest management problems are provided to illustrate how the technology works and the challenges deep learning faces. At least in the near term, its biggest impact on agrochemical development seems likely to come in automating the tedious work involved in assessing agrochemical efficacy, but getting there will require major investments in building large, well-curated data sets to work from and in providing the expertise required to assess the resulting model predictions in real-world scenarios. Deep learning may also come to complement the machine learning methodologies already available for use in pesticide discovery and development, but it seems unlikely to supplant them. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Building a Quantitative Structure-Property Relationship (QSPR) Model.

Knowing the physicochemical and general biochemical properties of a compound is critical to understanding how it behaves in different biological environments and to anticipating what is likely to happen in situations where that behavior cannot be measured directly. Quantitative structure-property relationship (QSPR) models provide a way to predict those properties even before a compound has been synthesized simply by knowing what its structure would be. This chapter describes a general workflow for compiling the data upon which a useful QSPR model is built, curating it, evaluating that model's performance, and then analyzing the predictive errors with an eye toward identifying systematic errors in the input data. The focus here is on models for the absorption, distribution, metabolism, and excretion (ADME) properties of drugs and toxins, but the considerations explored are general and applicable to any QSPR.

Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project.

The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure-activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.

A path to next-generation reproducibility in cheminformatics.

Currently, the submission guidelines for the Journal of Cheminformatics say it will "only publish research or software that is entirely reproducible by third parties." They go on to specify that being reproducible means that anything essential to the conclusion of the paper be freely accessible and states that source code must be provided. I submit that this definition of reproducibility is too narrow-that a cheminformatics method can only truly be replicated by reimplementing it from a detailed, step-by-step high-level description to determine how reliably the algorithm per se does what it is intended to do.

Predicting mammalian metabolism and toxicity of pesticides in silico.

Pesticides must be effective to be commercially viable but they must also be reasonably safe for those who manufacture them, apply them, or consume the food they are used to produce. Animal testing is key to ensuring safety, but it comes late in the agrochemical development process, is expensive, and requires relatively large amounts of material. Surrogate assays used as in vitro models require less material and shift identification of potential mammalian toxicity back to earlier stages in development. Modern in silico methods are cost-effective complements to such in vitro models that make it possible to predict mammalian metabolism, toxicity and exposure for a pesticide, crop residue or other metabolite before it has been synthesized. Their broader use could substantially reduce the amount of time and effort wasted in pesticide development. This contribution reviews the kind of in silico models that are currently available for vetting ideas about what to synthesize and how to focus development efforts; the limitations of those models; and the practical considerations that have slowed development in the area. Detailed discussions are provided of how bacterial mutagenicity, human cytochrome P450 (CYP) metabolism, and bioavailability in humans and rats can be predicted. © 2018 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Physiologically Based Pharmacokinetic Modeling in Lead Optimization. 2. Rational Bioavailability Design by Global Sensitivity Analysis To Identify Properties Affecting Bioavailability.

When medicinal chemists need to improve oral bioavailability (%F) during lead optimization, they systematically modify compound properties mainly based on their own experience and general rules of thumb. However, at least a dozen properties can influence %F, and the difficulty of multiparameter optimization for such complex nonlinear processes grows combinatorially with the number of variables. Furthermore, strategies can be in conflict. For example, adding a polar or charged group will generally increase solubility but decrease permeability. Identifying the 2 or 3 properties that most influence %F for a given compound series would make %F optimization much more efficient. We previously reported an adaptation of physiologically based pharmacokinetic (PBPK) simulations to predict %F for lead series from purely computational inputs within a 2-fold average error. Here, we run thousands of such simulations to generate a comprehensive "bioavailability landscape" for each series. A key innovation was recognition that the large and variable number of p Ka's in drug molecules could be replaced by just the two straddling the isoelectric point. Another was use of the ZINC database to cull out chemically inaccessible regions of property space. A quadratic partial least squares regression (PLS) accurately fits a continuous surface to these thousands of bioavailability predictions. The PLS coefficients indicate the globally sensitive compound properties. The PLS surface also displays the %F landscape in these sensitive properties locally around compounds of particular interest. Finally, being quick to calculate, the PLS equation can be combined with models for activity and other properties for multiobjective lead optimization.

Physiologically Based Pharmacokinetic Modeling in Lead Optimization. 1. Evaluation and Adaptation of GastroPlus To Predict Bioavailability of Medchem Series.

When medicinal chemists need to improve bioavailability (%F) within a chemical series during lead optimization, they synthesize new series members with systematically modified properties mainly by following experience and general rules of thumb. More quantitative models that predict %F of proposed compounds from chemical structure alone have proven elusive. Global empirical %F quantitative structure-property (QSPR) models perform poorly, and projects have too little data to train local %F QSPR models. Mechanistic oral absorption and physiologically based pharmacokinetic (PBPK) models simulate the dissolution, absorption, systemic distribution, and clearance of a drug in preclinical species and humans. Attempts to build global PBPK models based purely on calculated inputs have not achieved the <2-fold average error needed to guide lead optimization. In this work, local GastroPlus PBPK models are instead customized for individual medchem series. The key innovation was building a local QSPR for a numerically fitted effective intrinsic clearance (CLloc). All inputs are subsequently computed from structure alone, so the models can be applied in advance of synthesis. Training CLloc on the first 15-18 rat %F measurements gave adequate predictions, with clear improvements up to about 30 measurements, and incremental improvements beyond that.

Using Cheminformatics in Drug Discovery.

This chapter illustrates how cheminformatics can be applied to designing novel compounds that are active at the primary target and have good predicted ADMET properties. Examples of various cheminformatics techniques are illustrated in the process of designing inhibitors that inhibit both cyclooxygenase isoforms but are more potent toward COX-2. The first step in the process is to create a knowledge database of cyclooxygenase inhibitors in the public domain. This data was analyzed to find activity cliffs - small structural changes that result in drastic changes in potency. Additional cyclooxygenase potency and selectivity trends were obtained using matched molecular pair analysis. QSAR models were then developed to predict cyclooxygenase potency and selectivity. Next, computational algorithms were used to generate novel scaffolds starting from known cyclooxygenase inhibitors. Nine virtual libraries containing 240 compounds each were constructed. Predictions from the cyclooxygenase QSAR models were used to eliminate molecules with undesirable potency or selectivity. Additionally, the compounds were screened in silico for undesirable ADMET properties, e.g., low solubility, permeability, metabolic stability, or high toxicity, using a liability scoring system known as ADMET Risk™. Eight synthetic candidates were identified from this process after incorporating knowledge gained from activity cliff analysis. Four of the compounds were synthesized and tested to measure their COX-1 and COX-2 IC(50) values as well as several ADME properties. The best compound, SLP0020, had a COX-1 IC(50) of 770 nM and COX-2 IC(50) of 130 nM.

Tales from the war on error: the art and science of curating QSAR data.

Curating the data underlying quantitative structure-activity relationship models is a never-ending struggle. Some curation can now be automated but much cannot, especially where data as complex as those pertaining to molecular absorption, distribution, metabolism, excretion, and toxicity are concerned (vide infra). The authors discuss some particularly challenging problem areas in terms of specific examples involving experimental context, incompleteness of data, confusion of units, problematic nomenclature, tautomerism, and misapplication of automated structure recognition tools.

Best of both worlds: combining pharma data and state of the art modeling technology to improve in Silico pKa prediction.

In a unique collaboration between a software company and a pharmaceutical company, we were able to develop a new in silico pKa prediction tool with outstanding prediction quality. An existing pKa prediction method from Simulations Plus based on artificial neural network ensembles (ANNE), microstates analysis, and literature data was retrained with a large homogeneous data set of drug-like molecules from Bayer. The new model was thus built with curated sets of ∼14,000 literature pKa values (∼11,000 compounds, representing literature chemical space) and ∼19,500 pKa values experimentally determined at Bayer Pharma (∼16,000 compounds, representing industry chemical space). Model validation was performed with several test sets consisting of a total of ∼31,000 new pKa values measured at Bayer. For the largest and most difficult test set with >16,000 pKa values that were not used for training, the original model achieved a mean absolute error (MAE) of 0.72, root-mean-square error (RMSE) of 0.94, and squared correlation coefficient (R(2)) of 0.87. The new model achieves significantly improved prediction statistics, with MAE = 0.50, RMSE = 0.67, and R(2) = 0.93. It is commercially available as part of the Simulations Plus ADMET Predictor release 7.0. Good predictions are only of value when delivered effectively to those who can use them. The new pKa prediction model has been integrated into Pipeline Pilot and the PharmacophorInformatics (PIx) platform used by scientists at Bayer Pharma. Different output formats allow customized application by medicinal chemists, physical chemists, and computational chemists.

Using beta binomials to estimate classification uncertainty for ensemble models.

BACKGROUND: Quantitative structure-activity (QSAR) models have enormous potential for reducing drug discovery and development costs as well as the need for animal testing. Great strides have been made in estimating their overall reliability, but to fully realize that potential, researchers and regulators need to know how confident they can be in individual predictions. RESULTS: Submodels in an ensemble model which have been trained on different subsets of a shared training pool represent multiple samples of the model space, and the degree of agreement among them contains information on the reliability of ensemble predictions. For artificial neural network ensembles (ANNEs) using two different methods for determining ensemble classification - one using vote tallies and the other averaging individual network outputs - we have found that the distribution of predictions across positive vote tallies can be reasonably well-modeled as a beta binomial distribution, as can the distribution of errors. Together, these two distributions can be used to estimate the probability that a given predictive classification will be in error. Large data sets comprised of logP, Ames mutagenicity, and CYP2D6 inhibition data are used to illustrate and validate the method. The distributions of predictions and errors for the training pool accurately predicted the distribution of predictions and errors for large external validation sets, even when the number of positive and negative examples in the training pool were not balanced. Moreover, the likelihood of a given compound being prospectively misclassified as a function of the degree of consensus between networks in the ensemble could in most cases be estimated accurately from the fitted beta binomial distributions for the training pool. CONCLUSIONS: Confidence in an individual predictive classification by an ensemble model can be accurately assessed by examining the distributions of predictions and errors as a function of the degree of agreement among the constituent submodels. Further, ensemble uncertainty estimation can often be improved by adjusting the voting or classification threshold based on the parameters of the error distribution. Finally, the profiles for models whose predictive uncertainty estimates are not reliable provide clues to that effect without the need for comparison to an external test set.

Patient preferences in discussing sexual dysfunctions in primary care.

BACKGROUND AND OBJECTIVES: There are multiple barriers that limit patients and primary care physicians (PCPs) from discussing sexual dysfunctions (SDs) during medical appointments. Exploring patient preferences in discussing SDs with PCPs may help address some barriers, which can improve doctor-patient communication about SDs, delivery of health care, and patient quality of life. METHODS: A sample (n=108) of adult patients from an urban primary care clinic completed a 5--10 minute anonymous opinion questionnaire about their preferences in discussing SDs with PCPs. RESULTS: The majority of participants agreed that PCPs should give information to all patients (74%), ask all patients (69%), and have questions on medical history forms (55%) about SDs. Fifty-eight (58%) participants preferred to start the conversations about SDs with PCPs themselves, but all of these participants did not object to PCPs asking them about SDs. Participants who had ever experienced SD symptoms were more likely to want questions on medical history forms and for PCPs to ask about SDs. CONCLUSIONS: Participants preferred discussions about SDs with PCPs through various means (ie, medical history forms, medical appointments). Although participants were divided on who (patient versus PCP) should start conversations about SDs, the majority of participants did not object to PCPs inquiring about SDs during office visits or on medical history forms. Patients in poorer health and with self-reported SDs may need PCPs to inquire about SDs. Recommendations to improve health care delivery are suggested, including PCPs inquiring about SDs with all patients, especially with individuals in poorer health or with histories of SDs.