(Q)SAR Modeling and Safety Assessment in Regulatory Review

Isolation and in silico investigation of cannflavins from Cannabis sativa leaves as potential anti-SARS-CoV-2 agents targeting the Papain-Like Protease

This study aimed to isolate and identify three prenylflavonoids (cannflavin A, B, and C) from Cannabis sativa leaves using different chromatographic techniques. The potential of the isolated compounds against SARS-CoV-2 was suggested through several in silico analysis. Structural similarity studies against nine co-crystallized ligands of SARS-CoV-2's proteins indicated the similarities of the isolated cannflavins with the SARS-CoV-2 Papain-Like Protease (PLP) ligand, Y95. Then, flexible allignment study confirmed this similarity. Docking experiments showed successful binding of all cannflavins within the active pocket of PLP, with energies comparable to Y95. Among them, cannflavin A demonstrated the most similar binding mode, while cannflavin C exhibited the best energy. Molecular dynamics (MD) simulations and MM-GPSA confirmed the accurate binding of cannflavin A to the PLP. In silico ADMET studies indicated favourable drug-like properties for all three compounds, suggesting their potential as anti-SARS-CoV-2 agents. Further In vitro and In vivo investigations are necessary to validate these findings and establish their efficacy and safety profiles.

Integrated in silico and in vitro exploration of the anti-VEGFR-2 activities of a semisynthetic xanthine alkaloid inhibiting breast cancer

This study presents T-1-NBAB, a new compound derived from the natural xanthine alkaloid theobromine, aimed at inhibiting VEGFR-2, a crucial protein in angiogenesis. T-1-NBAB's potential to interacts with and inhibit the VEGFR-2 was indicated using in silico techniques like molecular docking, MD simulations, MM-GBSA, PLIP, essential dynamics, and bi-dimensional projection experiments. DFT experiments was utilized also to study the structural and electrostatic properties of T-1-NBAB. Computational analysis was performed to predict the ADME-Tox profiles of T-1-NBAB. After semisynthesis, the in vitro results showed that T-1-NBAB effectively inhibits VEGFR-2, with an IC50 of 0.115 μM, compared to sorafenib's 0.0591 μM. In vitro tests also demonstrated significant activity of T-1-NBAB against breast cancer cell lines MCF7 and T47D, with IC50 values of 16.88 μM and 61.17 μM, respectively, and high selectivity. Importantly, T-1-NBAB induced early and late apoptosis in MCF7 cells, indicating its potential as a strong anticancer agent. Additionally, T-1-NBAB reduced the migration and healing abilities of MCF7 cells, suggesting it could be a promising anti-angiogenic agent. Overall, these findings suggest that T-1-NBAB is a promising lead compound for further research as a potential treatment for breast cancer.

New thiazolidine-2,4-diones as potential anticancer agents and apoptotic inducers targeting VEGFR-2 kinase: Design, synthesis, in silico and in vitro studies

BACKGROUND

VEGFR-2 has emerged as a prominent positive regulator of cancer progression.

AIM

Discovery of new anticancer agents and apoptotic inducers targeting VEGFR-2.

METHODS

Design and synthesis of new thiazolidine-2,4-diones followed by extensive in vitro studies, including VEGFR-2 inhibition assay, MTT assay, apoptosis analysis, and cell migration assay. In silico investigations including docking, MD simulations, ADMET, toxicity, and DFT studies were performed.

RESULTS

Compound 15 showed the strongest VEGFR-2 inhibitory activity with an IC50 value of 0.066 μM. Additionally, most of the synthesized compounds showed anti-proliferative activity against HepG2 and MCF-7 cancer cell lines at the micromolar range with IC50 values ranging from 0.04 to 4.71 μM, relative to sorafenib (IC50 = 2.24 ± 0.06 and 3.17 ± 0.01 μM against HepG2 and MCF-7, respectively). Also, compound 15 showed selectivity indices of 1.36 and 2.08 against HepG2 and MCF-7, respectively. Furthermore, compound 15 showed a significant apoptotic effect and arrested the cell cycle of MCF-7 cells at the S phase. Moreover, compound 15 had a significant inhibitory effect on the ability of MCF-7 cells to heal from. Docking studies revealed that the synthesized thiazolidine-2,4-diones have a binding pattern approaching sorafenib. MD simulations indicated the stability of compound 15 in the active pocket of VEGFR-2 for 200 ns. ADMET and toxicity studies indicated an acceptable pharmacokinetic profile. DFT studies confirmed the ability of compound 15 to interact with VEGFR-2.

CONCLUSION

Compound 15 has promising anticancer activity targeting VEGFR-2 with significant activity as an apoptosis inducer.

New Theobromine Apoptotic Analogue with Anticancer Potential Targeting the EGFR Protein: Computational and In Vitro Studies

AIM

The aim of this study was to design and examine a novel epidermal growth factor receptor (EGFR) inhibitor with apoptotic properties by utilizing the essential structural characteristics of existing EGFR inhibitors as a foundation.

METHOD

The study began with the natural alkaloid theobromine and developed a new semisynthetic derivative (T-1-PMPA). Computational ADMET assessments were conducted first to evaluate its anticipated safety and general drug-likeness. Deep density functional theory (DFT) computations were initially performed to validate the three-dimensional (3D) structure and reactivity of T-1-PMPA. Molecular docking against the EGFR proteins was conducted to investigate T-1-PMPA's binding affinity and inhibitory potential. Additional molecular dynamics (MD) simulations over 200 ns along with MM-GPSA, PLIP, and principal component analysis of trajectories (PCAT) experiments were employed to verify the binding and inhibitory properties of T-1-PMPA. Afterward, T-1-PMPA was semisynthesized to validate the proposed design and in silico findings through several in vitro examinations.

RESULTS

DFT studies indicated T-1-PMPA's reactivity using electrostatic potential, global reactive indices, and total density of states. Molecular docking, MD simulations, MM-GPSA, PLIP, and ED suggested the binding and inhibitory properties of T-1-PMPA against the EGFR protein. The in silico ADMET predicted T-1-PMPA's safety and general drug-likeness. In vitro experiments demonstrated that T-1-PMPA effectively inhibited EGFRWT and EGFR790m, with IC50 values of 86 and 561 nM, respectively, compared to Erlotinib (31 and 456 nM). T-1-PMPA also showed significant suppression of the proliferation of HepG2 and MCF7 malignant cell lines, with IC50 values of 3.51 and 4.13 μM, respectively. The selectivity indices against the two cancer cell lines indicated the overall safety of T-1-PMPA. Flow cytometry confirmed the apoptotic effects of T-1-PMPA by increasing the total percentage of apoptosis to 42% compared to 31, and 3% in Erlotinib-treated and control cells, respectively. The qRT-PCR analysis further supported the apoptotic effects by revealing significant increases in the levels of Casp3 and Casp9. Additionally, T-1-PMPA controlled the levels of TNFα and IL2 by 74 and 50%, comparing Erlotinib's values (84 and 74%), respectively.

CONCLUSION

In conclusion, our study's findings suggest the potential of T-1-PMPA as a promising apoptotic anticancer lead compound targeting the EGFR.

Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across

We have modeled here chronic Daphnia toxicity taking pNOEC (negative logarithm of no observed effect concentration in mM) and pEC50 (negative logarithm of half-maximal effective concentration in mM) as endpoints using QSAR and chemical read-across approaches. The QSAR models were developed by strictly obeying the OECD guidelines and were found to be reliable, predictive, accurate, and robust. From the selected features in the developed models, we have found that an increase in lipophilicity and saturation, the presence of electrophilic or electronegative or heavy atoms, the presence of sulphur, amine, and their related functionality, an increase in mean atomic polarizability, and higher number of (thio-) carbamates (aromatic) groups are responsible for chronic toxicity. Therefore, this information might be useful for the development of environmentally friendly and safer chemicals and data-gap filling as well as reducing the use of identified toxic chemicals which have chronic toxic effects on aquatic ecosystems. Approved classes of drugs from DrugBank databases and diverse groups of chemicals from the Chemical and Product Categories (CPDat) database were also assessed through the developed models.

Computer aided drug discovery (CADD) of a thieno[2,3-d]pyrimidine derivative as a new EGFR inhibitor targeting the ribose pocket

Depending on the pharmacophoric characteristics of EGFR inhibitors, a new thieno[2,3-d]pyrimidine derivative has been developed. Firstly, the potential inhibitory effect of the designed compound against EGFR has been proven by docking experiments that showed correct binding modes and excellent binding energies of -98.44 and -88.00 kcal/mol, against EGFR wild-type and mutant type, respectively. Furthermore, MD simulations studies confirmed the precise energetic, conformational, and dynamic alterations that occurred after binding to EGFR. The correct binding was also confirmed by essential dynamics studies. To further investigate the general drug-like properties of the developed candidate, in silico ADME and toxicity studies have also been carried out. The thieno[2,3-d]pyrimidine derivative was synthesized following the earlier promising findings. Fascinatingly, the synthesized compound (4) showed promising inhibitory effects against EGFRWT and EGFRT790M with IC50 values of 25.8 and 182.3 nM, respectively. Also, it exhibited anticancer potentialities against A549 and MCF-7cell lines with IC50 values of 13.06 and 20.13 µM, respectively. Interestingly, these strong activities were combined with selectivity indices of 2.8 and 1.8 against the two cancer cell lines, respectively. Further investigations indicated the ability of compound 4 to arrest the cancer cells' growth at the G2/M phase and to increase early and late apoptosis percentages from 2.52% and 2.80 to 17.99% and 16.72%, respectively. Additionally, it was observed that compound 4 markedly increased the levels of caspase-3 and caspase-9 by 4 and 3-fold compared to the control cells. Moreover, it up-regulated the level of BAX by 3-fold and down-regulated the level of Bcl-2 by 3-fold affording a BAX/Bcl-2 ratio of 9.Communicated by Ramaswamy H. Sarma.

New Thieno[2,3-d]pyrimidines as Anticancer VEGFR-2 Inhibitors with Apoptosis Induction: Design, Synthesis, and Biological and In Silico Studies

BACKGROUND

Vascular endothelial growth factor receptor-2 (VEGFR-2) is a critical protein involved in tumor progression, making it an attractive target for cancer therapy.

OBJECTIVE

This study aimed to synthesize and evaluate novel thieno[2,3-d]pyrimidine analogues as potential anticancer VEGFR-2 inhibitors.

METHODS

The thieno[2,3-d]pyrimidine analogues were synthesized following the pharmacophoric features of VEGFR-2 inhibitors. The anticancer potential was assessed against PC3 and HepG2 cell lines. The VEGFR-2 inhibition was evaluated through IC50 determination. Cell cycle analysis and apoptosis assays were performed to elucidate the mechanisms of action. Molecular docking, molecular dynamics simulations, MM-GBSA, and PLIP studies were conducted to investigate the binding affinities and interactions with VEGFR-2. Additionally, in silico ADMET studies were performed.

RESULTS

Compound 8b demonstrated significant anti-proliferative activities with IC50 values of 16.35 μM and 8.24 μM against PC3 and HepG2 cell lines, respectively, surpassing sorafenib and exhibiting enhanced selectivity indices. Furthermore, compound 8b showed an IC50 value of 73 nM for VEGFR-2 inhibition. Cell cycle analysis revealed G2-M phase arrest, while apoptosis assays demonstrated increased apoptosis in HepG2 cells. Molecular docking and dynamic simulations confirmed the binding affinity and interaction of compound 8b with VEGFR-2, supported by MMGBSA and PLIP studies. In silico ADMET studies indicated the drug development potential of the synthesized thieno[2,3-d]pyrimidines.

CONCLUSION

The study highlights compound 8b as a promising VEGFR-2 inhibitor with potent anti-proliferative activities. Its mechanism of action involves cell cycle arrest and induction of apoptosis. Further, molecular docking and dynamic simulations support the strong binding affinity of compound 8b to VEGFR-2.

Computer-Assisted Drug Discovery of a Novel Theobromine Derivative as an EGFR Protein-Targeted Apoptosis Inducer

The overexpression of the Epidermal Growth Factor Receptor (EGFR) marks it as a pivotal target in cancer treatment, with the aim of reducing its proliferation and inducing apoptosis. This study aimed at the CADD of a new apoptotic EGFR inhibitor. The natural alkaloid, theobromine, was used as a starting point to obtain a new semisynthetic (di-ortho-chloro acetamide) derivative (T-1-DOCA). Firstly, T-1-DOCA's total electron density, energy gap, reactivity indices, and electrostatic surface potential were determined by DFT calculations, Then, molecular docking studies were carried out to predict the potential of T-1-DOCA against wild and mutant EGFR proteins. T-1-DOCA's correct binding was further confirmed by molecular dynamics (MD) over 100 ns, MM-GPSA, and PLIP experiments. In vitro, T-1-DOCA showed noticeable efficacy compared to erlotinib by suppressing EGFRWT and EGFRT790M with IC50 values of 56.94 and 269.01 nM, respectively. T-1-DOCA inhibited also the proliferation of H1975 and HCT-116 malignant cell lines, exhibiting IC50 values of 14.12 and 23.39 µM, with selectivity indices of 6.8 and 4.1, respectively, indicating its anticancer potential and general safety. The apoptotic effects of T-1-DOCA were indicated by flow cytometric analysis and were further confirmed through its potential to increase the levels of BAX, Casp3, and Casp9, and decrease Bcl-2 levels. In conclusion, T-1-DOCA, a new apoptotic EGFR inhibitor, was designed and evaluated both computationally and experimentally. The results suggest that T-1-DOCA is a promising candidate for further development as an anti-cancer drug.

New thiazolidine-2,4-diones as effective anti-proliferative and anti-VEGFR-2 agents: Design, synthesis, in vitro, docking, MD simulations, DFT, ADMET, and toxicity studies

Novel thiazolidine-2,4-dione derivatives, 11a-g, were designed, and synthesized targeting the VEGFR-2 protein. The in vitro studies indicated the abilities of the synthesized derivatives to inhibit VEGFR-2 and prevent the growth of two different cancer cell types, HepG2 and MCF-7. Compound 11 f exhibited the strongest anti-VEGFR-2 activity (IC50 = 0.053 µM). As well, compound 11 f showed impressive anti-proliferative activity against the mentioned cancer cell lines with IC50 values of 0.64 ± 0.01 and 0.53 ± 0.04 µM, respectively. Additionally, compound 11 f arrested the MCF-7 cell cycle at the S phase and increased the overall apoptosis percentage. Furthermore, cell migration assay revealed that compound 11 f has a significant ability to prevent migration and healing potentialities of MCF-7. Moreover, computational studies were used to conduct the molecular investigation of the VEGFR-2-11 f complex. The kinetic and structural features of the complex were examined using molecular dynamics simulations and molecular docking. Besides, Principal component analysis (PCA) was used to explain the dynamics of the VEGFR-2-11 f complex at various spatial scales. The DFT calculations also provided further clarity regarding compound 11 f's structural and electronic features. To evaluate how closely the developed compounds might look like drugs, ADMET and toxicity experiments were computed. To conclude, the presented study demonstrates the potential of compound 11 f as a viable anti-cancer drug, which can serve as a prototype for future structural modifications and further biological investigations.

Computer-assisted drug discovery (CADD) of an anti-cancer derivative of the theobromine alkaloid inhibiting VEGFR-2

VEGFR-2 is a significant target in cancer treatment, inhibiting angiogenesis and impeding tumor growth. Utilizing the essential pharmacophoric structural properties, a new semi-synthetic theobromine analogue (T-1-MBHEPA) was designed as VEGFR-2 inhibitor. Firstly, T-1-MBHEPA's stability and reactivity were indicated through several DFT computations. Additionally, molecular docking, MD simulations, MM-GPSA, PLIP, and essential dynamics (ED) experiments suggested T-1-MBHEPA's strong binding capabilities to VEGFR-2. Its computational ADMET profiles were also studied before the semi-synthesis and indicated a good degree of drug-likeness. T-1-MBHEPA was then semi-synthesized to evaluate the design and the in silico findings. It was found that, T-1-MBHEPA inhibited VEGFR-2 with an IC50 value of 0.121 ± 0.051 µM, as compared to sorafenib which had an IC50 value of 0.056 µM. Similarly, T-1-MBHEPA inhibited the proliferation of HepG2 and MCF7 cell lines with IC50 values of 4.61 and 4.85 µg/mL respectively - comparing sorafenib's IC50 values which were 2.24 µg/mL and 3.17 µg/mL respectively. Interestingly, T-1-MBHEPA revealed a noteworthy IC50 value of 80.0 µM against the normal cell lines exhibiting exceptionally high selectivity indexes (SI) of 17.4 and 16. 5 against the examined cell lines, respectively. T-1-MBHEPA increased the percentage of apoptotic MCF7 cells in early and late stages, respectively, from 0.71 % to 7.22 % and from 0.13 % to 2.72 %, while the necrosis percentage was increased to 11.41 %, in comparison to 2.22 % in control cells. Furthermore, T-1-MBHEPA reduced the production of pro-inflammatory cytokines TNF-α and IL-2 in the treated MCF7 cells by 33 % and 58 %, respectively indicating an additional anti-angiogenic mechanism. Also, T-1-MBHEPA decreased significantly the potentialities of MCF7 cells to heal and migrate from 65.9 % to 7.4 %. Finally, T-1-MBHEPA's oral treatment didn't show toxicity on the liver function (ALT and AST) and the kidney function (creatinine and urea) levels of mice.

Anti-breast cancer potential of a new xanthine derivative: In silico, antiproliferative, selectivity, VEGFR-2 inhibition, apoptosis induction and migration inhibition studies

BACKGROUND

The overexpression of VEGFR-2 receptors in breast cancer provides a valuable approach to anticancer strategies. Targeting VEGFR-2, a new semisynthetic compound (T-1-MCPAB) has been designed.

METHODS

Computational methods (ADMET, toxicity, DFT, Molecular Docking, Molecular Dynamics Simulations, MM-GBSA, PLIP, and PCAT) were conducted. In addition to the semi-synthesis, in vitro studies (anti-VEGFR-2, anti-proliferative, flow cytometry, and wound scratch assay) were employed.

RESULTS

ADME and toxicity profiles of T-1-MCPAB studies indicated its overall drug-likeness showing results much better than Sorafenib. Then, T-1-MCPAB's exact 3D structure, stability, and reactivity were evoked by the DFT calculations. Molecular docking, molecular dynamics simulations, MM-GPSA, PLIP, and PCAT studies denoted the correct binding and inhibiting potential of T-1-MCPAB, towards VEGFR-2 protein. After the semisynthesis, T-1-MCPAB inhibited VEGFR-2 with an IC50 of 0.135 µM, which was comparable to sorafenib's IC50 of 0.0591 µM. T-1-MCPAB also showed a notable performance against MCF7 and T47D breast cancer cell lines with IC50 values of 30.95 µM and 63.64 µM, respectively, and had high selectivity index values of 3.7 and 1.8, respectively. Furthermore, T-1-MCPAB influenced early and late apoptosis and significantly decreased the potential of MCF7 cells to heal and migrate.

CONCLUSION

T-1-MCPAB is a promising VEGFR-2 inhibitor with potential for breast cancer treatment. Further chemical and biological studies are needed to explore its potential as a therapeutic agent.

A Theobromine Derivative with Anticancer Properties Targeting VEGFR-2: Semisynthesis, in silico and in vitro Studies

A computer-assisted drug design (CADD) approach was utilized to design a new acetamido-N-(para-fluorophenyl)benzamide) derivative of the naturally occurring alkaloid, theobromine, (T-1-APFPB), following the pharmacophoric features of VEGFR-2 inhibitors. The stability and reactivity of T-1-AFPB were assessed through density functional theory (DFT) calculations. Molecular docking assessments showed T-1-AFPB's potential to bind with and inhibit VEGFR-2. The precise binding of T-1-AFPB against VEGFR-2 with optimal energy was further confirmed through several molecular dynamics (MD) simulations, PLIP, MM-GBSA, and PCA studies. Then, T-1-AFPB (4-(2-(3,7-Dimethyl-2,6-dioxo-2,3,6,7-tetrahydro-1H-purin-1-yl)acetamido)-N-(4-fluorophenyl)benzamide) was semi-synthesized and the in vitro assays showed its potential to inhibit VEGFR-2 with an IC50 value of 69 nM (sorafenib's IC50 was 56 nM) and to inhibit the growth of HepG2 and MCF-7 cancer cell lines with IC50 values of 2.24±0.02 and 3.26±0.02 μM, respectively. Moreover, T-1-AFPB displayed very high selectivity indices against normal Vero cell lines. Furthermore, T-1-AFPB induced early (from 0.72 to 19.12) and late (from 0.13 to 6.37) apoptosis in HepG2 cell lines. In conclusion, the combined computational and experimental approaches demonstrated the efficacy and safety of T-1-APFPB providing it as a promising lead VEGFR-2 inhibitor for further development aiming at cancer therapy.

Assessing the utility of common arguments used in expert review of in silico predictions as part of ICH M7 assessments

Expert review of two predictions, made by complementary (quantitative) structure-activity relationship models, to an overall conclusion is a key component of using in silico tools to assess the mutagenic potential of impurities as part of the ICH M7 guideline. In lieu of a specified protocol, numerous publications have presented best practise guides, often indicating the occurrence of common prediction scenarios and the evidence required to resolve them. A semi-automated expert review tool has been implemented in Lhasa Limited's Nexus platform following collation of these common arguments and assignment to the associated prediction scenarios made by Derek Nexus and Sarah Nexus. Using datasets primarily donated by pharmaceutical companies, an automated analysis of the frequency these prediction scenarios occur, and the likelihood of the associated arguments assigning the correct resolution, could then be conducted. This article highlights that a relatively small number of common arguments may be used to accurately resolve many prediction scenarios to a single conclusion. The use of a standardised method of argumentation and assessment of evidence for a given impurity is proposed to improve the efficiency and consistency of expert review as part of an ICH M7 submission.

A novel thieno[2,3-d]pyrimidine derivative inhibiting vascular endothelial growth factor receptor-2: A story of computer-aided drug discovery

Following the pharmacophoric features of vascular endothelial growth factor receptor 2 (VEGFR-2) inhibitors, a novel thieno[2,3-d]pyrimidine derivative has been designed and its activity against VEGFR-2 has been demonstrated by molecular docking studies that showed an accurate binding mode and an excellent binding energy. Furthermore, the recorded binding was confirmed by a series of molecular dynamics simulation studies, which also revealed precise energetic, conformational, and dynamic changes. Additionally, molecular mechanics with generalized Born and surface area solvation and polymer-induced liquid precursors studies were conducted and verified the results of the MD simulations. Next, in silico absorption, distribution, metabolism, excretion, and toxicity studies have also been conducted to examine the general drug-like nature of the designed candidate. According to the previous results, the thieno[2,3-d]pyrimidine derivative was synthesized. Fascinatingly, it inhibited VEGFR-2 (IC50  = 68.13 nM) and demonstrated strong inhibitory activity toward human liver (HepG2), and prostate (PC3) cell lines with IC50 values of 6.60 and 11.25 µM, respectively. As well, it was safe and showed a high selectivity index against normal cell lines (WI-38). Finally, the thieno[2,3-d]pyrimidine derivative arrested the growth of the HepG2 cells at the G2/M phase inducing both early and late apoptosis. These results were further confirmed through the ability of the thieno[2,3-d]pyrimidine derivative to induce significant changes in the apoptotic genes levels of caspase-3, caspase-9, Bcl-2 associated X-protein, and B-cell lymphoma 2.

Discovery of new thieno[2,3-d]pyrimidines as EGFR tyrosine kinase inhibitors for cancer treatment

Background: EGFR has been considered a vital molecular target in cancer management. Aim: The discovery of new thieno[2,3-d]pyrimidine derivatives as EGFR tyrosine kinase inhibitors. Methods: Nine derivatives were designed, synthesized and subjected to in vitro and in silico studies. Results: Compound 7a significantly inhibited the growth of HepG2 and PC3 cells for both EGFR wild-type and EGFRT790M. Compound 7a caused a significant apoptotic effect, arresting HepG2 cells' growth in the S and G2/M phases. Docking and molecular dynamics simulation studies confirmed the correct and stable binding modes of the synthesized compounds against the active sites. Conclusion: Compound 7a is a promising dual EGFR inhibitor for cancer treatment.

New science, drug regulation, and emergent public health issues: The work of FDA's division of applied regulatory science

The U.S. Food and Drug Administration (FDA) Division of Applied Regulatory Science (DARS) moves new science into the drug review process and addresses emergent regulatory and public health questions for the Agency. By forming interdisciplinary teams, DARS conducts mission-critical research to provide answers to scientific questions and solutions to regulatory challenges. Staffed by experts across the translational research spectrum, DARS forms synergies by pulling together scientists and experts from diverse backgrounds to collaborate in tackling some of the most complex challenges facing FDA. This includes (but is not limited to) assessing the systemic absorption of sunscreens, evaluating whether certain drugs can convert to carcinogens in people, studying drug interactions with opioids, optimizing opioid antagonist dosing in community settings, removing barriers to biosimilar and generic drug development, and advancing therapeutic development for rare diseases. FDA tasks DARS with wide ranging issues that encompass regulatory science; DARS, in turn, helps the Agency solve these challenges. The impact of DARS research is felt by patients, the pharmaceutical industry, and fellow regulators. This article reviews applied research projects and initiatives led by DARS and conducts a deeper dive into select examples illustrating the impactful work of the Division.

A New Anticancer Semisynthetic Theobromine Derivative Targeting EGFR Protein: CADDD Study

A new lead compound has been designed as an antiangiogenic EGFR inhibitor that has the pharmacophoric characteristics to bind with the catalytic pocket of EGFR protein. The designed lead compound is a (para-chloro)acetamide derivative of the alkaloid, theobromine, (T-1-PCPA). At first, we started with deep density functional theory (DFT) calculations for T-1-PCPA to confirm and optimize its 3D structure. Additionally, the DFT studies identified the electrostatic potential, global reactive indices and total density of states expecting a high level of reactivity for T-1-PCPA. Secondly, the affinity of T-1-PCPA to bind and inhibit the EGFR protein was studied and confirmed through detailed structure-based computational studies including the molecular docking against EGFR^WT and EGFR^T790M, Molecular dynamics (MD) over 100 ns, MM-GPSA and PLIP experiments. Before the preparation, the computational ADME and toxicity profiles of T-1-PCPA have been investigated and its safety and the general drug-likeness predicted. Accordingly, T-1-PCPA was semi-synthesized to scrutinize the proposed design and the obtained in silico results. Interestingly, T-1-PCPA inhibited in vitro EGFR^WT with an IC₅₀ value of 25.35 nM, comparing that of erlotinib (5.90 nM). Additionally, T-1-PCPA inhibited the growth of A549 and HCT-116 malignant cell lines with IC₅₀ values of 31.74 and 20.40 µM, respectively, comparing erlotinib that expressed IC₅₀ values of 6.73 and 16.35 µM, respectively.

Anticancer derivative of the natural alkaloid, theobromine, inhibiting EGFR protein: Computer-aided drug discovery approach

A new semisynthetic derivative of the natural alkaloid, theobromine, has been designed as a lead antiangiogenic compound targeting the EGFR protein. The designed compound is an (m-tolyl)acetamide theobromine derivative, (T-1-MTA). Molecular Docking studies have shown a great potential for T-1-MTA to bind to EGFR. MD studies (100 ns) verified the proposed binding. By MM-GBSA analysis, the exact binding with optimal energy of T-1-MTA was also identified. Then, DFT calculations were performed to identify the stability, reactivity, electrostatic potential, and total electron density of T-1-MTA. Furthermore, ADMET analysis indicated the T-1-MTA's general likeness and safety. Accordingly, T-1-MTA has been synthesized to be examined in vitro. Intriguingly, T-1-MTA inhibited the EGFR protein with an IC50 value of 22.89 nM and demonstrated cytotoxic activities against the two cancer cell lines, A549, and HCT-116, with IC50 values of 22.49, and 24.97 μM, respectively. Interestingly, T-1-MTA's IC50 against the normal cell lines, WI-38, was very high (55.14 μM) indicating high selectivity degrees of 2.4 and 2.2, respectively. Furthermore, the flow cytometry analysis of A549 treated with T-1-MTA showed significantly increased ratios of early apoptosis (from 0.07% to 21.24%) as well as late apoptosis (from 0.73% to 37.97%).

Design, Molecular Modeling, MD Simulations, Essential Dynamics, ADMET, DFT, Synthesis, Anti-proliferative, and Apoptotic Evaluations of a New Anti-VEGFR-2 Nicotinamide Analogue

OBJECTIVES

This study aims to design and evaluate (in silico and in vitro) a new nicotinamide derivative as an inhibitor of VEGFR-2, a major mediator of angiogenesis Methods: The following in silico studies were performed; DFT calculations, molecular modelling, MD simulations, MM-GBSA, PLIP, and PCAT studies. The compound's in silico (ADMET) analysis was also conducted. Subsequently, the compound ((E)-N-(4-(1-(2-(4-(4-Chlorobenzamido)benzoyl)hydrazono)ethyl) phenyl)nicotinamide) was successfully synthesized and designated as compound X. In vitro, VEGFR-2 inhibition and cytotoxicity of compound X against HCT-116 and A549 cancer cell lines and normal Vero cell lines were conducted. Apoptosis induction and migration assay of HCT-116 cell lines after treatment with compound X were also evaluated.

RESULTS

DFT calculations assigned stability and reactivity of compound X. Molecular docking and MD simulations indicated its excellent binding against VEGFR-2. Furthermore, MM-GBSA analysis, PLIP experiments, and PCAT studies confirmed compound X's correct binding with optimal dynamics and energy. ADMET analysis expressed its general likeness and safety. The in vitro assays demonstrated that compound X effectively inhibited VEGFR-2, with an IC50 value of 0.319 ± 0.013 μM and displayed cytotoxicity against HCT-116 and A549 cancer cell lines, with IC50 values of 57.93 and 78.82 μM, respectively. Importantly, compound X exhibited minimal toxicity towards the non-cancerous Vero cell lines, (IC50 = 164.12 μM). Additionally, compound X significantly induced apoptosis of HCT-116 cell lines and inhibited their potential to migrate and heal.

CONCLUSION

In summary, the presented study has identified compound X as a promising candidate for the development of a novel apoptotic lead anticancer drug.

(E)-N-(3-(1-(2-(4-(2,2,2-Trifluoroacetamido)benzoyl)hydrazono)ethyl)phenyl)nicotinamide: A Novel Pyridine Derivative for Inhibiting Vascular Endothelial Growth Factor Receptor-2: Synthesis, Computational, and Anticancer Studies

(E)-N-(3-(1-(2-(4-(2,2,2-Trifluoroacetamido)benzoyl)hydrazono)ethyl)phenyl)nicotinamide (compound 10) was designed as an antiangiogenic VEGFR-2 inhibitor with the essential pharmacophoric structural properties to interact with the catalytic pocket of VEGFR-2. The designed derivative was synthesized, and its structure was confirmed through Ms, elemental, ¹H, and ¹³C spectral data. The potentiality of the designed pyridine derivative to bind with and inhibit the vascular endothelial growth factor receptor-2 (VEGFR-2) enzyme was indicated by molecular docking assessments. In addition, six molecular dynamic (MD) experiments proved its correct binding with VEGFR-2 over 100 ns. Additionally, the molecular mechanics energies, combined with the generalized born and surface area (MM-GBSA) analysis, identified the precise binding with optimum energy. To explore the stability and reactivity of the designed pyridine derivative, density functional theory (DFT) calculations, including electrostatic potential maps and total electron density, were carried out. Additionally, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis demonstrated its general likeness and its safety. The designed compound was synthesized to evaluate its effects against VEGFR-2 protein, cancer, and normal cells. The in vitro results were concordant with the in silico results, because the new pyridine derivative (compound 10) displayed VEGFR-2 inhibition with an IC₅₀ value of 65 nM and displayed potent cytotoxic properties against hepatic (HepG2) and breast (MCF-7) cancer cell lines with IC₅₀ values of 21.00 and 26.10 μM, respectively; additionally, it exhibited high selectivity indices against the normal cell lines (W-38) of 1.55 and 1.25, respectively. The obtained results present compound 10 as a new lead VEGFR-2 inhibitor for further biological investigation and chemical modifications.

A New Theobromine-Based EGFRWT and EGFRT790M Inhibitor and Apoptosis Inducer: Design, Semi-Synthesis, Docking, DFT, MD Simulations, and In Vitro Studies

The essential pharmacophoric structural properties were applied to design a new derivative of theobromine as an antiangiogenic EGFR inhibitor. The designed candidate is a (para-nitrophenyl)acetamide derivative of the natural alkaloid, theobromine (T-2-PNPA). The potentialities of T-2-PNPA to inhibit the EGFR protein were studied computationally in an extensive way. Firstly, the molecular docking against EGFRWT and EGFRT790M demonstrated T-2-PNPA’s capabilities of binding with the targeted receptors. Then, the MD experiments (for 100 ns) illustrated through six different studies the changes that occurred in the energy as well as in the structure of EGFR–T-2-PNPA complex. Additionally, an MM-GBSA analysis determined the exact energy of binding and the essential residues. Furthermore, DFT calculations investigated the stability, reactivity, and electrostatic potential of T-2-PNPA. Finally, ADMET and toxicity studies confirmed both the safety as well as the general likeness of T-2-PNPA. Consequently, T-2-PNPA was prepared for the in vitro biological studies. T-2-PNPA inhibited EGFRWT and EGFRT790M with IC50 values of 7.05 and 126.20 nM, respectively, which is comparable with erlotinib activities (5.91 and 202.40, respectively). Interestingly, T-2-PNPA expressed cytotoxic potentialities against A549 and HCT-116 cells with IC50 values of 11.09 and 21.01 µM, respectively, which is again comparable with erlotinib activities (6.73 and 16.35, respectively). T-2-PNPA was much safer against WI-38 (IC50 = 48.06 µM) than erlotinib (IC50 = 31.17 µM). The calculated selectivity indices of T-2-PNPA against A549 and HCT-116 cells were 4.3 and 2.3, respectively. This manuscript presents a new lead anticancer compound (T-2-PNPA) that has been synthesized for the first time and exhibited promising in silico and in vitro anticancer potentialities.

New Anticancer Theobromine Derivative Targeting EGFRWT and EGFRT790M: Design, Semi-Synthesis, In Silico, and In Vitro Anticancer Studies

Based on the pharmacophoric features of EGFR inhibitors, a new semisynthetic theobromine-derived compound was designed to interact with the catalytic pocket of EGFR. Molecular docking against wild (EGFR^WT; PDB: 4HJO) and mutant (EGFR^T790M; PDB: 3W2O) types of EGFR-TK indicated that the designed theobromine derivative had the potential to bind to that pocket as an antiangiogenic inhibitor. The MD and MM-GBSA experiments identified the exact binding with optimum energy and dynamics. Additionally, the DFT calculations studied electrostatic potential, stability, and total electron density of the designed theobromine derivative. Both in silico ADMET and toxicity analyses demonstrated its general likeness and safety. We synthesized the designed theobromine derivative (compound XI) which showed an IC₅₀ value of 17.23 nM for EGFR inhibition besides IC₅₀ values of 21.99 and 22.02 µM for its cytotoxicity against A549 and HCT-116 cell lines, respectively. Interestingly, compound XI expressed a weak cytotoxic potential against the healthy W138 cell line (IC₅₀ = 49.44 µM, 1.6 times safer than erlotinib), exhibiting the high selectivity index of 2.2. Compound XI arrested the growth of A549 at the G2/M stage and increased the incidence of apoptosis.

Structure-Based Virtual Screening, Docking, ADMET, Molecular Dynamics, and MM-PBSA Calculations for the Discovery of Potential Natural SARS-CoV-2 Helicase Inhibitors from the Traditional Chinese Medicine

Continuing our antecedent work against COVID-19, a set of 5956 compounds of traditional Chinese medicine have been virtually screened for their potential against SARS-CoV-2 helicase (PDB ID: 5RMM). Initially, a fingerprint study with VXG, the ligand of the target enzyme, disclosed the similarity of 187 compounds. Then, a molecular similarity study declared the most similar 40 compounds. Subsequently, molecular docking studies were carried out to examine the binding modes and energies. Then, the most appropriate 26 compounds were subjected to in silico ADMET and toxicity studies to select the most convenient inhibitors to be: (1R,2S)-ephedrine (57), (1R,2S)-norephedrine (59), 2-(4-(pyrrolidin-1-yl)phenyl)acetic acid (84), 1-phenylpropane-1,2-dione (195), 2-methoxycinnamic acid (246), 2-methoxybenzoic acid (364), (R)-2-((R)-5-oxopyrrolidin-3-yl)-2-phenylacetic acid (405), (Z)-6-(3-hydroxy-4-methoxystyryl)-4-methoxy-2H-pyran-2-one (533), 8-chloro-2-(2-phenylethyl)-5,6,7-trihydroxy-5,6,7,8-tetrahydrochromone (637), 3-((1R,2S)-2-(dimethylamino)-1-hydroxypropyl)phenol (818), (R)-2-ethyl-4-(1-hydroxy-2-(methylamino)ethyl)phenol (5159), and (R)-2-((1S,2S,5S)-2-benzyl-5-hydroxy-4-methylcyclohex-3-en-1-yl)propane-1,2-diol (5168). Among the selected 12 compounds, the metabolites, compound 533 showed the best docking scores. Interestingly, the MD simulation studies for compound 533, the one with the highest docking score, over 100 ns showed its correct binding to SARS-CoV-2 helicase with low energy and optimum dynamics. Finally, MM-PBSA studies showed that 533 bonded favorably to SARS-CoV-2 helicase with a free energy value of −83 kJ/mol. Further, the free energy decomposition study determined the essential amino acid residues that contributed favorably to the binding process. The obtained results give a huge hope to find a cure for COVID-19 through further in vitro and in vivo studies for the selected compounds.

Using Jupyter Notebooks for re-training machine learning models

Machine learning (ML) models require an extensive, user-driven selection of molecular descriptors in order to learn from chemical structures to predict actives and inactives with a high reliability. In addition, privacy concerns often restrict the access to sufficient data, leading to models with a narrow chemical space. Therefore, we propose a framework of re-trainable models that can be transferred from one local instance to another, and further allow a less extensive descriptor selection. The models are shared via a Jupyter Notebook, allowing the evaluation and implementation of a broader chemical space by keeping most of the tunable parameters pre-defined. This enables the models to be updated in a decentralized, facile, and fast manner. Herein, the method was evaluated with six transporter datasets (BCRP, BSEP, OATP1B1, OATP1B3, MRP3, P-gp), which revealed the general applicability of this approach.

The Computational Preventive Potential of the Rare Flavonoid, Patuletin, Isolated from Tagetes patula, against SARS-CoV-2

The rare flavonoid, patuletin, was isolated from the flowers of Tagetes patula growing in Egypt. The rarity of the isolated compound inspired us to scrutinize its preventive effect against COVID-19 utilizing a multi-step computational approach. Firstly, a structural similarity study was carried out against nine ligands of nine SARS-CoV-2 proteins. The results showed a large structural similarity between patuletin and F86, the ligand of SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). Then, a 3D-Flexible alignment study of patuletin and F86 verified the proposed similarity. To determine the binding opportunity, patuletin was docked against the RdRp showing a correct binding inside its active pocket with an energy of −20 kcal/mol that was comparable to that of F86 (−23 kcal/mol). Following, several MD simulations as well as MM-PBSA studies authenticated the accurate binding of patuletin in the RdRp via the correct dynamic and energetic behaviors over 100 ns. Additionally, in silico ADMET studies showed the general safety and drug-likeness of patuletin.

Predicting zebrafish (Danio rerio) embryo developmental toxicity through a non-conformational QSAR approach

For many years, the frequent use of synthetic chemicals in the manufacture of veterinary drugs and plague control products has raised negative effects on human health and other non-target organisms, promoting the need to employ a practical and suitable methodology for early risk identification of several thousand commercial compounds. The zebrafish (Danio rerio) embryo has been emerged as one sustainable animal model for measuring developmental toxicity, an endpoint that is included in the regulatory procedures to approve chemicals, avoiding conventional and costly toxicity assays based on animal testing. In this context, the Quantitative Structure-Activity Relationships (QSAR) theory is applied to develop a predictive model based on a well-defined zebrafish embryo developmental toxicity database reported by the ToxCast™ Phase I chemical library of the Environmental Protection Agency (U.S. EPA). By means of four freely available softwares, a set with 28,038 non-conformational descriptors that encode the largest amount of permanent structural features are readily calculated. The Replacement Method (RM) variable subset selection technique provided the best regression models. Thereby, a linear QSAR model with proper statistical quality (R_train² = 0.64, RMSE_train = 0.49) is established in agreement with the Organization for Economic Co-operation and Development principles, accomplishing each internal (loo, l15 % o, VIF and Y-randomization) and external (R_test²,R_m², Q_F1², Q_F2², Q_F3² and CCC) validation criterion. The present QSAR approach provides a useful computational tool to estimate zebrafish developmental toxicity of new, untasted or hypothetical compounds, and it can contribute to the general lack of QSAR models in the literature to predict this endpoint.

Assessing the impact of expert knowledge on ICH M7 (Q)SAR predictions. Is expert review still needed?

The ICH M7 (R1) guideline recommends the use of complementary (Q)SAR models to assess the mutagenic potential of drug impurities as a state-of-the-art, high-throughput alternative to empirical testing. Additionally, it includes a provision for the application of expert knowledge to increase prediction confidence and resolve conflicting calls. Expert knowledge, which considers structural analogs and mechanisms of activity, has been valuable when models return an indeterminate (equivocal) result or no prediction (out-of-domain). A retrospective analysis of 1002 impurities evaluated in drug regulatory applications between April 2017 and March 2019 assessed the impact of expert review on (Q)SAR predictions. Expert knowledge overturned the default predictions for 26% of the impurities and resolved 91% of equivocal predictions and 75% of out-of-domain calls. Of the 261 overturned default predictions, 15% were upgraded to equivocal or positive and 79% were downgraded to equivocal or negative. Chemical classes with the most overturns were primary aromatic amines (46%), aldehydes (45%), Michael-reactive acceptors (37%), and non-primary alkyl halides (33%). Additionally, low confidence predictions were the most often overturned. Collectively, the results suggest that expert knowledge continues to play an important role in an ICH M7 (Q)SAR prediction workflow and triaging predictions based on chemical class and probability can improve (Q)SAR review efficiency.

Revisiting the bacterial mutagenicity assays: Report by a workgroup of the International Workshops on Genotoxicity Testing (IWGT)

The International Workshop on Genotoxicity Testing (IWGT) meets every four years to obtain consensus on unresolved issues associated with genotoxicity testing. At the 2017 IWGT meeting in Tokyo, four sub-groups addressed issues associated with the Organization for Economic Cooperation and Development (OECD) Test Guideline TG471, which describes the use of bacterial reverse-mutation tests. The strains sub-group analyzed test data from >10,000 chemicals, tested additional chemicals, and concluded that some strains listed in TG471 are unnecessary because they detected fewer mutagens than other strains that the guideline describes as equivalent. Thus, they concluded that a smaller panel of strains would suffice to detect most mutagens. The laboratory proficiency sub-group recommended (a) establishing strain cell banks, (b) developing bacterial growth protocols that optimize assay sensitivity, and (c) testing "proficiency compounds" to gain assay experience and establish historical positive and control databases. The sub-group on criteria for assay evaluation recommended that laboratories (a) track positive and negative control data; (b) develop acceptability criteria for positive and negative controls; (c) optimize dose-spacing and the number of analyzable doses when there is evidence of toxicity; (d) use a combination of three criteria to evaluate results: a dose-related increase in revertants, a clear increase in revertants in at least one dose relative to the concurrent negative control, and at least one dose that produced an increase in revertants above control limits established by the laboratory from historical negative controls; and (e) establish experimental designs to resolve unclear results. The in silico sub-group summarized in silico utility as a tool in genotoxicity assessment but made no specific recommendations for TG471. Thus, the workgroup identified issues that could be addressed if TG471 is revised. The companion papers (a) provide evidence-based approaches, (b) recommend priorities, and (c) give examples of clearly defined terms to support revision of TG471.

Development of improved QSAR models for predicting the outcome of the in vivo micronucleus genetic toxicity assay

All drugs entering clinical trials are expected to undergo a series of in vitro and in vivo genotoxicity tests as outlined in the International Council on Harmonization (ICH) S2 (R1) guidance. Among the standard battery of genotoxicity tests used for pharmaceuticals, the in vivo micronucleus assay, which measures the frequency of micronucleated cells mostly from blood or bone marrow, is recommended for detecting clastogens and aneugens. (Quantitative) structure-activity relationship [(Q)SAR] models may be used as early screening tools by pharmaceutical companies to assess genetic toxicity risk during drug candidate selection. Models can also provide decision support information during regulatory review as part of the weight-of-evidence when experimental data are insufficient. In the present study, two commercial (Q)SAR platforms were used to construct in vivo micronucleus models from a recently enhanced in-house database of non-proprietary study findings in mice. Cross-validated performance statistics for the new models showed sensitivity of up to 74% and negative predictivity of up to 86%. In addition, the models demonstrated cross-validated specificity of up to 77% and coverage of up to 94%. These new models will provide more reliable predictions and offer an investigational approach for drug safety assessment with regards to identifying potentially genotoxic compounds.

Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses

The International Council on Harmonisation (ICH) M7(R1) guideline describes the use of complementary (quantitative) structure-activity relationship ((Q)SAR) models to assess the mutagenic potential of drug impurities in new and generic drugs. Historically, the CASE Ultra and Leadscope software platforms used two different statistical-based models to predict mutations at G-C (guanine-cytosine) and A-T (adenine-thymine) sites, to comprehensively assess bacterial mutagenesis. In the present study, composite bacterial mutagenicity models covering multiple mutation types were developed. These new models contain more than double the number of chemicals (n = 9,254 and n = 13,514) than the corresponding non-composite models and show better toxicophore coverage. Additionally, the use of a single composite bacterial mutagenicity model simplifies impurity analysis in an ICH M7 (Q)SAR workflow by reducing the number of model outputs requiring review. An external validation set of 388 drug impurities representing proprietary pharmaceutical chemical space showed performance statistics ranging from of 66-82% in sensitivity, 91-95% in negative predictivity and 96% in coverage. This effort represents a major enhancement to these (Q)SAR models and their use under ICH M7(R1), leading to improved patient safety through greater predictive accuracy, applicability, and efficiency when assessing the bacterial mutagenic potential of drug impurities.

Downstream Processing Technologies/Capturing and Final Purification : Opportunities for Innovation, Change, and Improvement. A Review of Downstream Processing Developments in Protein Purification

Increased pressure on upstream processes to maximize productivity has been crowned with great success, although at the cost of shifting the bottleneck to purification. As drivers were economical, focus is on now on debottlenecking downstream processes as the main drivers of high manufacturing cost. Devising a holistically efficient and economical process remains a key challenge. Traditional and emerging protein purification strategies with particular emphasis on methodologies implemented for the production of recombinant proteins of biopharmaceutical importance are reviewed. The breadth of innovation is addressed, as well as the challenges the industry faces today, with an eye to remaining impartial, fair, and balanced. In addition, the scope encompasses both chromatographic and non-chromatographic separations directed at the purification of proteins, with a strong emphasis on antibodies. Complete solutions such as integrated USP/DSP strategies (i.e., continuous processing) are discussed as well as gains in data quantity and quality arising from automation and high-throughput screening (HTS). Best practices and advantages through design of experiments (DOE) to access a complex design space such as multi-modal chromatography are reviewed with an outlook on potential future trends. A discussion of single-use technology, its impact and opportunities for further growth, and the exciting developments in modeling and simulation of DSP rounds out the overview. Lastly, emerging trends such as 3D printing and nanotechnology are covered. Graphical Abstract Workflow of high-throughput screening, design of experiments, and high-throughput analytics to understand design space and design space boundaries quickly. (Reproduced with permission from Gregory Barker, Process Development, Bristol-Myers Squibb).

A review on machine learning methods for in silico toxicity prediction

In silico toxicity prediction plays an important role in the regulatory decision making and selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time, budget, and other resources. Many computational methods have been employed in predicting the toxicity profile of chemicals. This review provides a detailed end-to-end overview of the application of machine learning algorithms to Structure-Activity Relationship (SAR)-based predictive toxicology. From raw data to model validation, the importance of data quality is stressed as it greatly affects the predictive power of derived models. Commonly overlooked challenges such as data imbalance, activity cliff, model evaluation, and definition of applicability domain are highlighted, and plausible solutions for alleviating these challenges are discussed.

Construction and application of (Q)SAR models to predict chemical-induced in vitro chromosome aberrations

In drug development, genetic toxicology studies are conducted using in vitro and in vivo assays to identify potential mutagenic and clastogenic effects, as outlined in the International Council for Harmonisation (ICH) S2 regulatory guideline. (Quantitative) structure-activity relationship ((Q)SAR) models that predict assay outcomes can be used as an early screen to prioritize pharmaceutical candidates, or later during product development to evaluate safety when experimental data are unavailable or inconclusive. In the current study, two commercial QSAR platforms were used to build models for in vitro chromosomal aberrations in Chinese hamster lung (CHL) and Chinese hamster ovary (CHO) cells. Cross-validated CHL model predictive performance showed sensitivity of 80 and 82%, and negative predictivity of 75 and 76% based on 875 training set compounds. For CHO, sensitivity of 61 and 67% and negative predictivity of 68 and 74% was achieved based on 817 training set compounds. The predictive performance of structural alerts in a commercial expert rule-based SAR software was also investigated and showed positive predictivity of 48-100% for selected alerts. Case studies examining incorrectly-predicted compounds, non-DNA-reactive clastogens, and recently-approved pharmaceuticals are presented, exploring how an investigational approach using similarity searching and expert knowledge can improve upon individual (Q)SAR predictions of the clastogenicity of drugs.

In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

Translating New Science Into the Drug Review Process: The US FDA's Division of Applied Regulatory Science

In 2011, the US Food and drug Administration (FDA) developed a strategic plan for regulatory science that focuses on developing new tools, standards, and approaches to assess the safety, efficacy, quality, and performance of FDA-regulated products. In line with this, the Division of Applied Regulatory Science was created to move new science into the Center for Drug Evaluation and Research (CDER) review process and close the gap between scientific innovation and drug review. The Division, located in the Office of Clinical Pharmacology, is unique in that it performs mission-critical applied research and review across the translational research spectrum including in vitro and in vivo laboratory research, in silico computational modeling and informatics, and integrated clinical research covering clinical pharmacology, experimental medicine, and postmarket analyses. The Division collaborates with Offices throughout CDER, across the FDA, other government agencies, academia, and industry. The Division is able to rapidly form interdisciplinary teams of pharmacologists, biologists, chemists, computational scientists, and clinicians to respond to challenging regulatory questions for specific review issues and for longer-range projects requiring the development of predictive models, tools, and biomarkers to speed the development and regulatory evaluation of safe and effective drugs. This article reviews the Division's recent work and future directions, highlighting development and validation of biomarkers; novel humanized animal models; translational predictive safety combining in vitro, in silico, and in vivo clinical biomarkers; chemical and biomedical informatics tools for safety predictions; novel approaches to speed the development of complex generic drugs, biosimilars, and antibiotics; and precision medicine.

Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

Background

In recent years, research in artificial neural networks has resurged, now under the deep-learning umbrella, and grown extremely popular. Recently reported success of DL techniques in crowd-sourced QSAR and predictive toxicology competitions has showcased these methods as powerful tools in drug-discovery and toxicology research. The aim of this work was dual, first large number of hyper-parameter configurations were explored to investigate how they affect the performance of DNNs and could act as starting points when tuning DNNs and second their performance was compared to popular methods widely employed in the field of cheminformatics namely Naïve Bayes, k-nearest neighbor, random forest and support vector machines. Moreover, robustness of machine learning methods to different levels of artificially introduced noise was assessed. The open-source Caffe deep-learning framework and modern NVidia GPU units were utilized to carry out this study, allowing large number of DNN configurations to be explored.

Results

We show that feed-forward deep neural networks are capable of achieving strong classification performance and outperform shallow methods across diverse activity classes when optimized. Hyper-parameters that were found to play critical role are the activation function, dropout regularization, number hidden layers and number of neurons. When compared to the rest methods, tuned DNNs were found to statistically outperform, with p value <0.01 based on Wilcoxon statistical test. DNN achieved on average MCC units of 0.149 higher than NB, 0.092 than kNN, 0.052 than SVM with linear kernel, 0.021 than RF and finally 0.009 higher than SVM with radial basis function kernel. When exploring robustness to noise, non-linear methods were found to perform well when dealing with low levels of noise, lower than or equal to 20%, however when dealing with higher levels of noise, higher than 30%, the Naïve Bayes method was found to perform well and even outperform at the highest level of noise 50% more sophisticated methods across several datasets.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-017-0226-y) contains supplementary material, which is available to authorized users.

Current nonclinical testing paradigms in support of safe clinical trials: An IQ Consortium DruSafe perspective

The transition from nonclinical to First-in-Human (FIH) testing is one of the most challenging steps in drug development. In response to serious outcomes in a recent Phase 1 trial (sponsored by Bial), IQ Consortium/DruSafe member companies reviewed their nonclinical approach to progress small molecules safely to FIH trials. As a common practice, safety evaluation begins with target selection and continues through iterative in silico and in vitro screening to identify molecules with increased probability of acceptable in vivo safety profiles. High attrition routinely occurs during this phase. In vivo exploratory and pivotal FIH-enabling toxicity studies are then conducted to identify molecules with a favorable benefit-risk profile for humans. The recent serious incident has reemphasized the importance of nonclinical testing plans that are customized to the target, the molecule, and the intended clinical plan. Despite the challenges and inherent risks of transitioning from nonclinical to clinical testing, Phase 1 studies have a remarkably good safety record. Given the rapid scientific evolution of safety evaluation, testing paradigms and regulatory guidance must evolve with emerging science. The authors posit that the practices described herein, together with science-based risk assessment and management, support safe FIH trials while advancing development of important new medicines.

Marshall SF, Burghaus R, Cosson V, Cheung SYA, Chenel M, DellaPasqua O, Frey N, Hamrén B, Harnisch L, Ivanow F, Kerbusch T, Lippert J, Milligan PA, Rohou S, Staab A, Steimer JL, Tornøe C, Visser SAG. Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation

This document was developed to enable greater consistency in the practice, application, and documentation of Model-Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of "good practice" recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit R&D efficiency; ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines.

ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling

Background

Determination of acute toxicity, expressed as median lethal dose (LD₅₀), is one of the most important steps in drug discovery pipeline. Because in vivo assays for oral acute toxicity in mammals are time-consuming and costly, there is thus an urgent need to develop in silico prediction models of oral acute toxicity.

Results

In this study, based on a comprehensive data set containing 7314 diverse chemicals with rat oral LD₅₀ values, relevance vector machine (RVM) technique was employed to build the regression models for the prediction of oral acute toxicity in rate, which were compared with those built using other six machine learning approaches, including k-nearest-neighbor regression, random forest (RF), support vector machine, local approximate Gaussian process, multilayer perceptron ensemble, and eXtreme gradient boosting. A subset of the original molecular descriptors and structural fingerprints (PubChem or SubFP) was chosen by the Chi squared statistics. The prediction capabilities of individual QSAR models, measured by q_ext² for the test set containing 2376 molecules, ranged from 0.572 to 0.659.

Conclusion

Considering the overall prediction accuracy for the test set, RVM with Laplacian kernel and RF were recommended to build in silico models with better predictivity for rat oral acute toxicity. By combining the predictions from individual models, four consensus models were developed, yielding better prediction capabilities for the test set (q_ext² = 0.669–0.689). Finally, some essential descriptors and substructures relevant to oral acute toxicity were identified and analyzed, and they may be served as property or substructure alerts to avoid toxicity. We believe that the best consensus model with high prediction accuracy can be used as a reliable virtual screening tool to filter out compounds with high rat oral acute toxicity. Graphical abstract

Taking Advantage of Databases

Toxicity databases are a useful resource to support hazard and risk assessment. They are used to retrieve historical studies for compounds of interest and to support toxicity predictions where no data exists. Toxicity predictions are either based upon study results from similar chemicals or prediction models built from these databases.

Encompassing receptor flexibility in virtual screening using ensemble docking-based hybrid QSAR: discovery of novel phytochemicals for BACE1 inhibition

Mimicking receptor flexibility during receptor-ligand binding is a challenging task in computational drug design since it is associated with a large increase in the conformational search space. In the present study, we have devised an in silico design strategy incorporating receptor flexibility in virtual screening to identify potential lead compounds as inhibitors for flexible proteins. We have considered BACE1 (β-secretase), a key target protease from a therapeutic perspective for Alzheimer's disease, as the highly flexible receptor. The protein undergoes significant conformational transitions from open to closed form upon ligand binding, which makes it a difficult target for inhibitor design. We have designed a hybrid structure-activity model containing both ligand based descriptors and energetic descriptors obtained from molecular docking based on a dataset of structurally diverse BACE1 inhibitors. An ensemble of receptor conformations have been used in the docking study, further improving the prediction ability of the model. The designed model that shows significant prediction ability judged by several statistical parameters has been used to screen an in house developed 3-D structural library of 731 phytochemicals. 24 highly potent, novel BACE1 inhibitors with predicted activity (Ki) ≤ 50 nM have been identified. Detailed analysis reveals pharmacophoric features of these novel inhibitors required to inhibit BACE1.

(Q)SAR: A Tool for the Toxicologist

A continuing education (CE) course at the 2014 American College of Toxicology annual meeting covered the topic of (Quantitative) Structure-Activity Relationships [(Q)SAR]. The (Q)SAR methodologies use predictive computer modeling based on predefined rules to describe the relationship between chemical structure and a chemical's associated biological activity or statistical tools to find correlations between biologic activity and the molecular structure or properties of a compound. The (Q)SAR has applications in risk assessment, drug discovery, and regulatory decision making. Pressure within industry to reduce the cost of drug development and societal pressure for government regulatory agencies to produce more accurate and timely risk assessment of drugs and chemicals have necessitated the use of (Q)SAR. Producing a high-quality (Q)SAR model depends on many factors including the choice of statistical methods and descriptors, but first and foremost the quality of the data input into the model. Understanding how a (Q)SAR model is developed and applied is critical to the successful use of such a tool. The CE session covered the basic principles of (Q)SAR, practical applications of these computational models in toxicology, how regulatory agencies use and interpret (Q)SAR models, and potential pitfalls of using them.

Hologram QSAR models of a series of 6-arylquinazolin-4-amine inhibitors of a new Alzheimer's disease target: dual specificity tyrosine-phosphorylation-regulated kinase-1A enzyme

Dual specificity tyrosine-phosphorylation-regulated kinase-1A (DYRK1A) is an enzyme directly involved in Alzheimer's disease, since its increased expression leads to β-amyloidosis, Tau protein aggregation, and subsequent formation of neurofibrillary tangles. Hologram quantitative structure-activity relationship (HQSAR, 2D fragment-based) models were developed for a series of 6-arylquinazolin-4-amine inhibitors (36 training, 10 test) of DYRK1A. The best HQSAR model (q2 = 0.757; SEcv = 0.493; R2 = 0.937; SE = 0.251; R2pred = 0.659) presents high goodness-of-fit (R2 > 0.9), as well as high internal (q2 > 0.7) and external (R2pred > 0.5) predictive power. The fragments that increase and decrease the biological activity values were addressed using the colored atomic contribution maps provided by the method. The HQSAR contribution map of the best model is an important tool to understand the activity profiles of new derivatives and may provide information for further design of novel DYRK1A inhibitors.

At the bench: the key role of PK-PD modelling in enabling the early discovery of biologic therapies

Pharmacokinetic-pharmacodynamic (PK-PD) modelling is already used extensively in pre-clinical and clinical drug development to characterize drug candidates quantitatively, aid go/no-go decisions and to inform future trial design and optimal dosing regimens. Less well known, although arguably as powerful, is its application at the earliest stages of drug development, at target selection and lead selection, where these same techniques can be used to predict and so bring forward drug candidates with the necessary characteristics or, for unachievable requirements, allow the abandonment of the programme for the minimum spend of time and cost. We consider three examples that illustrate the power of the application of modelling at this early stage. We start with the simple case of determining the optimal characteristics for a monoclonal antibody against a soluble ligand with its application to the investment decision for the development of best-in-class compounds. This is extended to the more complex situation of the target protein having an endogenous, inhibitory binding protein. We then illustrate how using physiologically-based pharmacokinetic modelling enables the appropriate engineering and testing of biological therapeutics for optimal PK-PD characteristics. These examples illustrate how a minimal investment in modelling achieves orders of magnitude better returns in choosing the correct targets, mechanism of action and candidate characteristics to progress to clinical trials, streamlining drug development and delivering better medicines to patients.

(Q)SAR assessments of potentially mutagenic impurities: a regulatory perspective on the utility of expert knowledge and data submission

(Quantitative) structure activity relationship [(Q)SAR] modeling is the primary tool used to evaluate the mutagenic potential associated with drug impurities. General recommendations regarding the use of (Q)SAR in regulatory decision making have recently been provided in the ICH M7 guideline. Although (Q)SAR alone is capable of achieving reasonable sensitivity and specificity, reliance on a simple positive or negative prediction can be problematic. The key to improving (Q)SAR performance is to integrate supporting information, also referred to as expert knowledge, into the final conclusion. In the regulatory context, expert knowledge is intended to (1) maximize confidence in a (Q)SAR prediction, (2) provide rationale to supersede a positive or negative (Q)SAR prediction, or (3) provide a basis for assessing mutagenicity in absence of a (Q)SAR prediction. Expert knowledge is subjective and is associated with great variability in regards to content and quality. However, it is still a critical component of impurity evaluations and its utility is acknowledged in the ICH M7 guideline. The current paper discusses the use of expert knowledge to support regulatory decision making, describes case studies, and provides recommendations for reporting data from (Q)SAR evaluations.

Applying machine learning techniques for ADME-Tox prediction: a review

INTRODUCTION

Pharmacokinetics involves the study of absorption, distribution, metabolism, excretion and toxicity of xenobiotics (ADME-Tox). In this sense, the ADME-Tox profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safety were considered some of the major causes of clinical failures in the development of new chemical entities. In this context, machine learning (ML) techniques have been often used in ADME-Tox studies due to the existence of compounds with known pharmacokinetic properties available for generating predictive models.

AREAS COVERED

This review examines the growth in the use of some ML techniques in ADME-Tox studies, in particular supervised and unsupervised techniques. Also, some critical points (e.g., size of the data set and type of output variable) must be considered during the generation of models that relate ADME-Tox properties and biological activity.

EXPERT OPINION

ML techniques have been successfully employed in pharmacokinetic studies, helping the complex process of designing new drug candidates from the use of reliable ML models. An application of this procedure would be the prediction of ADME-Tox properties from studies of quantitative structure-activity relationships or the discovery of new compounds from a virtual screening using filters based on results obtained from ML techniques.