Emerging opportunities of using large language models for translation between drug molecules and indications.

A drug molecule is a substance that changes an organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications (which describes the disease, condition or symptoms for which the drug is used), or vice versa. Addressing this challenge could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI.

Advances in Nanotechnology for Enhancing the Solubility and Bioavailability of Poorly Soluble Drugs.

This manuscript offers a comprehensive overview of nanotechnology's impact on the solubility and bioavailability of poorly soluble drugs, with a focus on BCS Class II and IV drugs. We explore various nanoscale drug delivery systems (NDDSs), including lipid-based, polymer-based, nanoemulsions, nanogels, and inorganic carriers. These systems offer improved drug efficacy, targeting, and reduced side effects. Emphasizing the crucial role of nanoparticle size and surface modifications, the review discusses the advancements in NDDSs for enhanced therapeutic outcomes. Challenges such as production cost and safety are acknowledged, yet the potential of NDDSs in transforming drug delivery methods is highlighted. This contribution underscores the importance of nanotechnology in pharmaceutical engineering, suggesting it as a significant advancement for medical applications and patient care.

Quality by Design Perspective for Designing Foam-based Formulation: Current State of Art.

Foam-based delivery systems contain one or more active ingredients and dispersed solid or liquid components that transform into gaseous form when the valve is actuated. Foams are an attractive and effective delivery approach for medical, cosmetic, and pharmaceutical uses. The foams-based delivery systems are gaining attention due to ease of application as they allow direct application onto the affected area of skin without using any applicator or finger, hence increasing the compliance and satisfaction of the patients. In order to develop foam-based delivery systems with desired qualities, it is vital to understand which type of material and process parameters impact the quality features of foams and which methodologies may be utilized to investigate foams. For this purpose, Quality-by-Design (QbD) approach is used. It aids in achieving quality-based development during the development process by employing the QbD concept. The critical material attributes (CMAs) and critical process parameters (CPPs) were discovered through the first risk assessment to ensure the requisite critical quality attributes (CQAs). During the initial risk assessment, the high-risk CQAs were identified, which affect the foam characteristics. In this review, the authors discussed the various CMAs, CPPs, CQAs, and risk factors associated in order to develop an ideal foam-based formulation with desired characteristics.

Selective detection of azelnidipine in pharmaceuticals via carbon dot mediated spectrofluorimetric method: A green approach.

A spectrofluorimetric method using fluorescent carbon dots (CDs) was developed for the selective detection of azelnidipine (AZEL) pharmaceutical in the presence of other drugs. In this study, N-doped CDs (N-CDs) were synthesized through a single-step hydrothermal process, using citric acid and urea as precursor materials. The prepared N-CDs showed a highly intense blue fluorescence emission at 447 nm, with a photoluminescence quantum yield of ~21.15% and a fluorescence lifetime of 0.47 ns. The N-CDs showed selective fluorescence quenching in the presence of all three antihypertensive drugs, which was used as a successful detection platform for the analysis of AZEL. The photophysical properties, UV-vis light absorbance, fluorescence emission, and lifetime measurements support the interaction between N-CDs and AZEL, leading to fluorescence quenching of N-CDs as a result of ground-state complex formation followed by a static fluorescence quenching phenomenon. The detection platform showed linearity in the range 10-200 µg/ml (R2 = 0.9837). The developed method was effectively utilized for the quantitative analysis of AZEL in commercially available pharmaceutical tablets, yielding results that closely align with those obtained from the standard method (UV spectroscopy). With a score of 0.76 on the 'Analytical GREEnness (AGREE)' scale, the developed analytical method, incorporating 12 distinct green analytical chemistry components, stands out as an important technique for estimating AZEL.

A sustainable sensitive spectrofluorimetric approach for the determination of the multipotent protein lactoferrin in different pharmaceuticals and infant milk formula: Compliance with greenness metrics.

The current study presents the first spectrofluorimetric approach for the estimation of lactoferrin, depending on the measurement of its native fluorescence at 337 nm after excitation at 230 nm, without the need for any hazardous chemicals or reagents. It was found that the fluorescence intensity versus concentration calibration plot was linear over the concentration range of 0.1-10.0 µg/mL with quantitation and detection limits of 0.082 and 0.027 µg/mL, respectively. The method was accordingly validated according to the ICH recommendations. The developed method was applied for the estimation of lactoferrin in different dosage forms, including capsules and sachets with high percent recoveries (97.84-102.53) and low %RSD values (<1.95). Lactoferrin is one of the key nutrients in milk powder and a significant nutritional fortifier. In order to assess the quality of milk powder, it is essential to rapidly and accurately quantify the lactoferrin content of the product. Therefore, the presented study was successfully applied for the selective estimation of lactoferrin in milk powder with acceptable percent recoveries (96.45-104.92) and %RSD values (≤3.607). Finally, the green profile of the method was estimated using two assessment tools: Green Analytical Procedure Index (GAPI) and Analytical GREEnness (AGREE), which demonstrated its excellent greenness.

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue.

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel quantitative structure-activity relationship (QSAR) method to predict a parameter more precisely from an incomplete data set via optimizing data handling by making use of predicted explanatory variables. As a case study we focused on the tissue-to-plasma partition coefficient (Kp), which is an important parameter for understanding drug distribution in tissues and building the physiologically based pharmacokinetic model and is a representative of small and sparse data sets. In this study, we predicted the Kp values of 119 compounds in nine tissues (adipose, brain, gut, heart, kidney, liver, lung, muscle, and skin), although some of these were not available. To fill the missing values in Kp for each tissue, first we predicted those Kp values by the nonmissing data set using a random forest (RF) model with in vitro parameters (log P, fu, Drug Class, and fi) like a classical prediction by a QSAR model. Next, to predict the tissue-specific Kp values in a test data set, we constructed a second RF model with not only in vitro parameters but also the Kp values of other tissues (i.e., other than target tissues) predicted by the first RF model as explanatory variables. Furthermore, we tested all possible combinations of explanatory variables and selected the model with the highest predictability from the test data set as the final model. The evaluation of Kp prediction accuracy based on the root-mean-square error and R2 value revealed that the proposed models outperformed other machine learning methods such as the conventional RF and message-passing neural networks. Significant improvements were observed in the Kp values of adipose tissue, brain, kidney, liver, and skin. These improvements indicated that the Kp information on other tissues can be used to predict the same for a specific tissue. Additionally, we found a novel relationship between each tissue by evaluating all combinations of explanatory variables. In conclusion, we developed a novel RF model to predict Kp values. We hope that this method will be applied to various problems in the field of experimental biology which often contains missing values in the near future.

Revisiting drug-protein interaction prediction: a novel global-local perspective.

MOTIVATION: Accurate inference of potential drug-protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI prediction, limiting their performance. RESULTS: We propose a new computational framework that integrates global and local features of nodes in the drug-protein bipartite graph for efficient DPI inference. Initially, we employ pre-trained models to acquire fundamental knowledge of drugs and proteins and to determine their initial features. Subsequently, the MinHash and HyperLogLog algorithms are utilized to estimate the similarity and set cardinality between drug and protein subgraphs, serving as their local features. Then, an energy-constrained diffusion mechanism is integrated into the transformer architecture, capturing interdependencies between nodes in the drug-protein bipartite graph and extracting their global features. Finally, we fuse the local and global features of nodes and employ multilayer perceptrons to predict the likelihood of potential DPIs. A comprehensive and precise node representation guarantees efficient prediction of unknown DPIs by the model. Various experiments validate the accuracy and reliability of our model, with molecular docking results revealing its capability to identify potential DPIs not present in existing databases. This approach is expected to offer valuable insights for furthering drug repurposing and personalized medicine research. AVAILABILITY AND IMPLEMENTATION: Our code and data are accessible at: https://github.com/ZZCrazy00/DPI.

Towards Greener and More Cost-efficient Biosynthesis of Pharmaceuticals and Fragrance Molecules.

Enzymes are natural catalysts which are gaining momentum in chemical synthesis due to their exquisiteselectivity and their biodegradability. However, the cost-efficiency and the sustainability of the overall biocatalytic process must be enhanced to unlock completely the potential of enzymes for industrial applications. To reach this goal, enzyme immobilization and the integration into continuous flow reactors have been the cornerstone of our research. We showed key examples of the advantages of those tools for the biosynthesis of antivirals, anticancer drugs, and valuable fragrance molecules. By combining new strategies to immobilize biocatalysts, innovative bioengineering approaches, and process development, the performance of the reactions could be boosted up to 100-fold.

Green electrospun Janus membrane of polyether block amide (PEBA) doped with hierarchical magnesium hydrogen phosphate for the removal of pharmaceuticals and personal care products.

It has been a challenge to prepared polyether block amide (PEBA) fibrous membrane via solution electrospinning. The only few reported methods though involved hazardous solvents and surfactants which were against the principle of green chemistry. In this work, uniform fibrous membrane of PEBA was successfully fabricated by solution electrospinning with a bio-based solvent dihydrolevoglucosenone (Cyrene). To further improve the mechanical strength and adsorption performance of the PEBA membrane, a hierarchical magnesium hydrogen phosphate (MgHPO4·1.2H2O, MHP) was synthesized to blend evenly into the PEBA matrix. A Janus MHP/PEBA membrane with one side of hydrophobic surface and the other side of hydrophilic surface was subsequently prepared, which exhibited fast adsorption, high capacity, good selectivity and reusability towards ibuprofen, acetaminophen, carbamazepine and triclosan. In addition, the Janus membrane showed high removal efficiency of the above contaminants in secondary wastewater effluent with good long term stability. It demonstrated that this Janus MHP/PEBA membrane had a good potential in practical wastewater treatment.

Enhanced properties of a positive-charged nanofiltration membrane containing quaternarized chitosan through second interfacial polymerization for the removal of salts and pharmaceuticals.

Nanofiltration (NF) membrane technology has been widely used in the removal of salts and trace organic pollutants, such as pharmaceuticals and personal care products (PPCPs), due to its superiority. A positive-charged composite NF membrane with an active skin layer was prepared by polyethyleneimine (PEI), trimethyl benzene chloride, and quaternate chitosan (HTCC) through second interfacial polymerization on the polyethersulfone ultrafiltration membrane. The physicochemical properties of the nanocomposite membrane were investigated using surface morphology, hydrophilicity, surface charge, and molecular weight cut-off (MWCO). The influence of the concentration and reaction time of PEI and HTCC was documented. The optimized membrane had a MWCO of about 481 Da and possessed a pure water permeability of 25.37 L·m-2·h-1·MPa-1. The results also exhibited salt rejection ability as MgCl2 > CaCl2 > MgSO4 > Na2SO4 > NaCl > KCl, showing a positive charge on the fabricated membrane. In addition, the membrane had higher rejection to atenolol, carbamazepine, amlodipine, and ibuprofen at 89.46, 86.02, 90.12, and 77.21%, respectively. Moreover, the anti-fouling performance and stability of the NF membrane were also improved.

Application of covalent modality in proximity-induced drug pharmacology: Early development, current strategy, and feature directions.

With a growing number of covalent drugs securing FDA approval as successful therapies across various indications, particularly in the realm of cancer treatment, the covalent modulating strategy is undergoing a resurgence. The renewed interest in covalent bioactive compounds has captured significant attention from both the academic and biopharmaceutical industry sectors. Covalent chemistry presents several advantages over traditional noncovalent proximity-induced drugs, including heightened potency, reduced molecular size, and the ability to target "undruggable" entities. Within this perspective, we have compiled a comprehensive overview of current covalent modalities applied to proximity-induced molecules, delving into their advantages and drawbacks. Our aim is to stimulate more profound insights and ideas within the scientific community, guiding future research endeavors in this dynamic field.

Determination of genotoxic impurities of aromatic aldehydes in pharmaceutical preparations by high performance liquid chromatography after derivatization with N-Cyclohexyl-4-hydrazino-1,8-naphthalenediimide.

The detection of aromatic aldehydes, considered potential genotoxic impurities, holds significant importance during drug development and production. Current analytical methods necessitate complex pre-treatment processes and exhibit insufficient specificity and sensitivity. This study presents the utilization of naphthalenediimide as a pre-column derivatisation reagent to detect aromatic aldehyde impurities in pharmaceuticals via high-performance liquid chromatography (HPLC). We screened a series of derivatisation reagents through density functional theory (DFT) and investigated the phenomenon of photoinduced electron transfer (PET) for both the derivatisation reagents and the resulting products. Optimal experimental conditions for derivatisation were achieved at 40 °C for 60 min. This approach has been successfully applied to detect residual aromatic aldehyde genotoxic impurities in various pharmaceutical preparations, including 4-Nitrobenzaldehyde, 2-Nitrobenzaldehyde, 1,4-Benzodioxane-6-aldehyde, and 5-Hydroxymethylfurfural. The pre-column derivatisation method significantly enhanced detection sensitivity and reduced the limit of detection (LOD), which ranged from 0.002 to 0.008 µg/ml for the analytes, with relative standard deviations < 3 %. The correlation coefficient (R2) >0.998 demonstrated high quality. In chloramphenicol eye drops, the concentration of 4-Nitrobenzaldehyde was measured to be 8.6 µg/mL below the specified concentration, with recoveries ranging from 90.0 % to 119.2 %. In comparison to existing methods, our work simplifies the pretreatment process, enhances the sensitivity and specificity of the analysis, and offers comprehensive insights into impurity detection in pharmaceutical preparations.

Improved assay development of pharmaceutical modalities using feedback-controlled liquid chromatography optimization.

Development of meaningful and reliable analytical assays in the (bio)pharmaceutical industry can often be challenging, involving tedious trial and error experimentation. In this work, an automated analytical workflow using an AI-based algorithm for streamlined method development and optimization is presented. Chromatographic methods are developed and optimized from start to finish by a feedback-controlled modeling approach using readily available LC instrumentation and software technologies, bypassing manual user intervention. With the use of such tools, the time requirement of the analyst is drastically minimized in the development of a method. Herein key insights on chromatography system control, automatic optimization of mobile phase conditions, and final separation landscape for challenging multicomponent mixtures are presented (e.g., small molecules drug, peptides, proteins, and vaccine products) showcased by a detailed comparison of a chiral method development process. The work presented here illustrates the power of modern chromatography instrumentation and AI-based software to accelerate the development and deployment of new separation assays across (bio)pharmaceutical modalities while yielding substantial cost-savings, method robustness, and fast analytical turnaround.

Deep eutectic solvents as green and sustainable diluents in headspace gas chromatography for the determination of trace level genotoxic impurities in pharmaceuticals.

Genotoxic impurities (GTIs) are potential carcinogens that need to be controlled down to ppm or lower concentration levels in pharmaceuticals under strict regulations. The static headspace gas chromatography (HS-GC) coupled with electron capture detection (ECD) is an effective approach to monitor halogenated and nitroaromatic genotoxins. Deep eutectic solvents (DESs) possess tunable physico-chemical properties and low vapor pressure for HS-GC methods. In this study, zwitterionic and non-ionic DESs have been used for the first time to develop and validate a sensitive analytical method for the analysis of 24 genotoxins at sub-ppm concentrations. Compared to non-ionic diluents, zwitterionic DESs produced exceptional analytical performance and the betaine : 7 (1,4- butane diol) DES outperformed the betaine : 5 (1,4-butane diol) DES. Limits of detection (LOD) down to the 5-ppb concentration level were achieved in DESs. Wide linear ranges spanning over 5 orders of magnitude (0.005-100 µg g-1) were obtained for most analytes with exceptional sensitivities and high precision. The method accuracy and precision were validated using 3 commercially available drug substances and excellent recoveries were obtained. This study broadens the applicability of HS-GC in the determination of less volatile GTIs by establishing DESs as viable diluent substitutes for organic solvents in routine pharmaceutical analysis.

Validation of a Caco-2 microfluidic Chip model for predicting intestinal absorption of BCS Class I-IV drugs.

Oral delivery is considered the most patient preferred route of drug administration, however, the drug must be sufficiently soluble and permeable to successfully formulate an oral formulation. There have been advancements in the development of more predictive solubility and dissolution tools, but the tools that has been developed for permeability assays have not been validated as extensively as the gold-standard Caco-2 Transwell assay. Here, we evaluated Caco-2 intestinal permeability assay in Transwells and a commercially available microfluidic Chip using 19 representative Biopharmaceutics Classification System (BCS) Class I-IV compounds. For each selected compound, we performed a comprehensive viability test, quantified its apparent permeability (Papp), and established an in vitro in vivo correlation (IVIVC) to the human fraction absorbed (fa) in both culture conditions. Permeability differences were observed across the models as demonstrated by antipyrine (Transwell Papp: 38.5 ± 6.1 × 10-8 cm/s vs Chip Papp: 32.9 ± 11.3 × 10-8 cm/s) and nadolol (Transwell Papp: 0.6 ± 0.1 × 10-7 cm/s vs Chip Papp: 3 ± 1.2 × 10-7 cm/s). The in vitro in vivo correlation (IVIVC; Papp vs. fa) of the Transwell model (r2 = 0.59-0.83) was similar to the Chip model (r2 = 0.41-0.79), highlighting similar levels of predictivity. Comparing to historical data, our Chip Papp data was more closely aligned to native tissues assessed in Ussing chambers. This is the first study to comprehensively validate a commercial Gut-on-a-Chip model as a predictive tool for assessing oral absorption to further reduce our reliance on animal models.

Force-controlled release of small molecules with a rotaxane actuator.

Force-controlled release of small molecules offers great promise for the delivery of drugs and the release of healing or reporting agents in a medical or materials context1-3. In polymer mechanochemistry, polymers are used as actuators to stretch mechanosensitive molecules (mechanophores)4. This technique has enabled the release of molecular cargo by rearrangement, as a direct5,6 or indirect7-10 consequence of bond scission in a mechanophore, or by dissociation of cage11, supramolecular12 or metal complexes13,14, and even by 'flex activation'15,16. However, the systems described so far are limited in the diversity and/or quantity of the molecules released per stretching event1,2. This is due to the difficulty in iteratively activating scissile mechanophores, as the actuating polymers will dissociate after the first activation. Physical encapsulation strategies can be used to deliver a larger cargo load, but these are often subject to non-specific (that is, non-mechanical) release3. Here we show that a rotaxane (an interlocked molecule in which a macrocycle is trapped on a stoppered axle) acts as an efficient actuator to trigger the release of cargo molecules appended to its axle. The release of up to five cargo molecules per rotaxane actuator was demonstrated in solution, by ultrasonication, and in bulk, by compression, achieving a release efficiency of up to 71% and 30%, respectively, which places this rotaxane device among the most efficient release systems achieved so far1. We also demonstrate the release of three representative functional molecules (a drug, a fluorescent tag and an organocatalyst), and we anticipate that a large variety of cargo molecules could be released with this device. This rotaxane actuator provides a versatile platform for various force-controlled release applications.

Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods.

Half-life is a significant pharmacokinetic parameter included in the excretion phase of absorption, distribution, metabolism, and excretion. It is one of the key factors for the successful marketing of drug candidates. Therefore, predicting half-life is of great significance in drug design. In this study, we employed eXtreme Gradient Boosting (XGboost), randomForest (RF), gradient boosting machine (GBM), and supporting vector machine (SVM) to build quantitative structure-activity relationship (QSAR) models on 3512 compounds and evaluated model performance by using root-mean-square error (RMSE), R2, and mean absolute error (MAE) metrics and interpreted features by SHapley Additive exPlanation (SHAP). Furthermore, we developed consensus models through integrating four individual models and validated their performance using a Y-randomization test and applicability domain analysis. Finally, matched molecular pair analysis was used to extract the transformation rules. Our results revealed that XGboost outperformed other individual models (RMSE = 0.176, R2 = 0.845, MAE = 0.141). The consensus model integrating all four models continued to enhance prediction performance (RMSE = 0.172, R2 = 0.856, MAE = 0.138). We evaluated the reliability, robustness, and generalization ability via Y-randomization test and applicability domain analysis. Meanwhile, we utilized SHAP to interpret features and employed matched molecular pair analysis to extract chemical transformation rules that provide suggestions for optimizing drug structure. In conclusion, we believe that the consensus model developed in this study serve as a reliable tool to evaluate half-life in drug discovery, and the chemical transformation rules concluded in this study could provide valuable suggestions in drug discovery.

Sorting drug conformers in enzyme active sites: the XTB way.

In the present study, we have used the MEI196 set of interaction energies to investigate low-cost computational chemistry approaches for the calculation of binding between a molecule and its environment. Density functional theory (DFT) methods, when used with the vDZP basis set, yield good agreement with the reference energies. On the other hand, semi-empirical methods are less accurate as expected. By examining different groups of systems within MEI196 that contain species of a similar nature, we find that chemical similarity leads to cancellation of errors in the calculation of relative binding energies. Importantly, the semi-empirical method GFN1-xTB (XTB1) yields reasonable results for this purpose. We have thus further assessed the performance of XTB1 for calculating relative energies of docking poses of substrates in enzyme active sites represented by cluster models or within the ONIOM protocol. The results support the observations on error cancellation. This paves the way for the use of XTB1 in parts of large-scale virtual screening workflows to accelerate the drug discovery process.

Recent developments in the use of nanocrystals to improve bioavailability of APIs.

Nanocrystals refer to materials with at least one dimension smaller than 100 nm, composing of atoms arranged in single crystals or polycrystals. Nanocrystals have significant research value as they offer unique advantages over conventional pharmaceutical formulations, such as high bioavailability, enhanced targeting selectivity and controlled release ability and are therefore suitable for the delivery of a wide range of drugs such as insoluble drugs, antitumor drugs and genetic drugs with broad application prospects. In recent years, research on nanocrystals has been progressively refined and new products have been launched or entered the clinical phase of studies. However, issues such as safety and stability still stand that need to be addressed for further development of nanocrystal formulations, and significant gaps do exist in research in various fields in this pharmaceutical arena. This paper presents a systematic overview of the advanced development of nanocrystals, ranging from the preparation approaches of nanocrystals with which the bioavailability of poorly water-soluble drugs is improved, critical properties of nanocrystals and associated characterization techniques, the recent development of nanocrystals with different administration routes, the advantages and associated limitations of nanocrystal formulations, the mechanisms of physical instability, and the enhanced dissolution performance, to the future perspectives, with a final view to shed more light on the future development of nanocrystals as a means of optimizing the bioavailability of drug candidates. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles.

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.