Growth score-based drug candidate screening method
By using a growth score-based method in drug screening, subdividing the impact of each stage of the bacterial growth cycle, the problem of incomplete assessment of the impact of candidate drug growth cycle in the prior art is solved, and more efficient and accurate drug screening and evaluation are achieved. .
Patent Information
- Application Number
- CN202411451932.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-17
- Publication Date
- 2025-02-11
AI Technical Summary
The existing drug screening methods lack a comprehensive assessment of candidate drugs at all stages of the cell growth cycle, resulting in insufficient understanding of potential pharmacological activities and drug action mechanisms. The data interpretation is complex and resource-intensive, limiting the scope of evaluable candidate drugs.
Using a growth score-based drug screening method, the "growth score" during the entire growth cycle is calculated by decomposing the geometric shape of the bacterial growth curve to comprehensively evaluate the impact of candidates on the cell growth cycle and preferential drug candidates with high scores. .
A comprehensive quantitative assessment of the impact of candidate drugs on each stage of the cell growth cycle is achieved, which improves the efficiency and accuracy of drug screening, and can more accurately identify drug molecules with potential pharmacological activities and predict their potential effects and safety.
Smart Images
Figure CN119418753A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the fields of biotechnology, microbiology and drug research and development, and in particular to a drug candidate screening method based on growth score. Background Art
[0002] In the field of drug development, screening of candidate drugs is a key link to preclinical and clinical research, and directly determines which compounds can enter further preclinical and clinical research. However, current drug screening methods focus on evaluating the specific biological activities of drugs, such as receptor binding, enzyme inhibition or signal pathway regulation, and the survival status of bacteria. However, these methods have some limitations. These methods often only consider a single biological effect, ignoring the potential impact of drugs on multiple stages of the cell growth cycle, and lack a holistic perspective to fully display the comprehensive effects of drugs. In particular, for the lag phase of the bacterial non-growth phase (i.e., an important preparatory stage before cell division), bacteria are protected from the effects of antibiotics and their tolerance is increased. In addition, the complexity of data interpretation and the large amount of resources and time required for traditional methods further increase the uncertainty and cost of the drug screening process, limiting the scope of candidate drugs that can be evaluated.
[0003] Therefore, there is an urgent need for a new evaluation parameter and screening method that can more comprehensively reflect the specific effects of candidate drugs on various stages of the cell growth cycle. This method can not only more accurately identify drug molecules with potential pharmacological activity, but also provide important clues for a deeper understanding of the drug's mechanism of action, thereby promoting progress and innovation in drug research and development. In the field of biology and microbiology, "growth "Growth metrics" are an indispensable tool set for evaluating the growth of microorganisms (especially bacteria), covering multiple dimensions from cell density to metabolic activity. These metrics include but are not limited to optical density (OD) - a rapid assessment of cell density through light absorption at a wavelength of 600nm; colony forming units (CFU) - a direct count reflecting the ability of cells to survive and proliferate; growth rate and doubling time, which quantify how quickly the number of cells changes over time and how long it takes to double, respectively; biomass, which measures the total amount of cells per unit volume; specific growth rate (μ), which assesses the growth efficiency of a unit of biomass; yield, which is the efficiency of converting nutrients into biomass or products; metabolic activity, which monitors the metabolic state of cells through respiration rate; fluorescence measurement, which is used to detect the expression of specific cellular components; scattered light measurement, which indirectly assesses cell density; dissolved oxygen tension (DOT), which is a key indicator of cell metabolic activity and culture environment; and activity detection, which ensures that cells maintain their vital characteristics.
[0004] Although growth metrics cover multiple aspects ranging from cell density and survival rate to metabolic activity, a significant shortcoming when analyzing the growth process of microorganisms is that they often do not fully consider the analysis of the "lag phase".
[0005] The lag phase (Lag) is the period of time from when microorganisms are inoculated into fresh culture medium to when they adapt to the new environment and before they begin logarithmic growth. During this stage, microbial cells undergo a series of physiological adjustments, such as repairing damage, synthesizing necessary enzymes and metabolites, and adapting to new temperatures, pH values, and nutritional conditions. However, since these adjustment processes do not immediately manifest as growth in cell numbers, traditional growth metrics (such as OD, CFU, growth rate, etc.) may not provide accurate information during the Lag phase.
[0006] Ignoring the analysis of the Lag phase may lead to an incomplete understanding of the growth dynamics of microorganisms. For example, when evaluating the effects of antibiotics or environmental stress on microbial growth, if one only focuses on changes in the logarithmic growth phase or the stationary phase, the potential effects of these stresses on microbial cells in the Lag phase may be ignored, thereby underestimating their overall effects.
[0007] In order to have a more comprehensive understanding of the growth process of microorganisms, especially for drug screening, methods to accurately monitor and quantify the lag phase are needed. Currently, these include combining the use of multiple growth metrics, such as simultaneous monitoring of OD and metabolic activity, and the use of more advanced single-cell analysis techniques to observe changes in cell behavior during the Lag phase. In addition, mathematical models and bioinformatics tools are used to conduct in-depth analysis of growth data to reveal the regulatory mechanisms and influencing factors of the Lag phase. These methods require more complex methods for detection and are more expensive. Summary of the invention
[0008] To overcome the problem that existing technologies require more complex methods for testing, which is costly and limits the range of candidate drugs that can be evaluated.
[0009] The technical solution of the present invention is: a drug candidate screening method based on growth score, which calculates the "growth score" by quantifying the effect of the candidate on the cell growth cycle, and uses the "growth score" to sort the candidates to give priority to drug candidates with high scores.
[0010] As a preference, the specific steps are as follows:
[0011] S1: Decomposing the geometry of bacterial growth curves
[0012] S11: Lag period: During this period, the bacteria adapt to the new environment, and the growth rate is extremely low. The curve is a nearly horizontal straight line. By fitting the data of this stage, the length of the lag period and its corresponding growth rate can be determined.
[0013] S12: Exponential growth phase: The bacterial growth rate is constant, and the curve is a straight line with a constant slope. The rapid proliferation ability of bacteria is evaluated by calculating the slope of the straight line in this phase;
[0014] S13: Stable phase: resources are limited, the growth rate slows down to zero, the curve tends to be horizontal, and the duration of this phase and the final cell density are recorded;
[0015] S2: Definition and calculation of “growth score”
[0016] S21: The geometric shapes of the above three stages are considered as a collection of bacterial growth trajectories, where the area (or integral) of each stage represents the cumulative growth of the number of cells in that stage;
[0017] S22: Define “growth score” as the sum of the areas (or integrals) of all stages during the entire growth cycle, which comprehensively reflects the overall growth capacity of bacteria from inoculation to the stationary phase;
[0018] S23: Through mathematical formulas or algorithms, the geometric parameters of each stage are converted into calculation factors of "growth scores" to ensure the accuracy and comparability of the scores;
[0019] S3: Drug Screening Applications
[0020] S31: Plot bacterial growth curves under different drug treatment conditions and perform geometric shape decomposition;
[0021] S32: Calculate the growth score under each drug treatment and compare it with the control group without drug treatment;
[0022] S33: Based on the degree of reduction in the growth score, the inhibitory effect of the drug on bacterial growth is evaluated and drug candidates with potential therapeutic effects are screened out.
[0023] Preferably, the geometric parameters in S23 include length, slope and final density.
[0024] Beneficial effects of the present invention:
[0025] 1. The concept of "growth score" is introduced to quantify the effect of candidates on the entire cell growth cycle, providing a new parameter for the screening of drug candidates to comprehensively evaluate the effect of cell growth cycle. This method helps to give priority to high-value candidates for new drug discovery, and scientifically and systematically screen and evaluate their potential pharmacological value by accurately quantifying the specific effect of candidates on the cell growth cycle. This strategy is not only efficient and accurate, but also particularly suitable for the early discovery stage of antibacterial drugs, ensuring the accurate identification of drug molecules with potential antibacterial activity, and being able to more accurately predict the potential effect and safety of candidates, thereby improving the efficiency and success rate of new drug discovery;
[0026] 2. Comprehensiveness: Through geometric shape decomposition, the entire process of bacterial growth is fully considered, including the lag phase, exponential growth phase and stable phase, which improves the accuracy of growth assessment;
[0027] 3. Quantitativeness: Introducing "growth score" as a quantitative indicator facilitates direct comparison of growth status under different experimental conditions and drug treatments;
[0028] 4. Efficiency: The geometric shape-based analysis method simplifies the data processing process and improves the efficiency and accuracy of drug screening. BRIEF DESCRIPTION OF THE DRAWINGS
[0029] Figure 1 What is shown is a dynamic record of the bacterial growth cycle, where (a) is the complete growth cycle that the bacteria undergo after inoculation under standard or control conditions, and (b) is the bacterial growth curve under treatment;
[0030] Figure 2 The “growth score” comparison of the effect of candidate drugs on bacterial growth is shown, where (a) is a control group without candidate treatment, and (b) is a group treated with different candidate drugs;
[0031] Figure 3 The demonstration shows the clustering or ranking of candidates using the "growth score";
[0032] Figure 4 Shown is the direct effect on the viability of mycobacteria. DETAILED DESCRIPTION
[0033] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.
[0034] See also Figure 1-Figure 4 The present invention provides an embodiment: a method for screening drug candidates based on growth score, the steps of which are as follows:
[0035] 1. Record and monitor the impact on each stage of cell growth through bacterial culture technology, and use the Bioscreen automatic growth curve analyzer to monitor the dynamic record of bacterial growth cycle;
[0036] like Figure 1 (a) shows the complete growth cycle of bacteria after inoculation under standard or control conditions, including the adaptation period (Lag phase), rapid proliferation in the logarithmic growth period (Log phase), and finally entering the stationary phase (Stationary phase). In this group, all strains were not subjected to any additional treatment during the growth process, so they showed similar growth curves with no significant differences;
[0037] like Figure 1 (b) shows the bacterial growth curve under treatment, which may produce different growth effects, such as delaying the entry into the logarithmic growth phase, reducing the growth rate or increasing the final cell density.
[0038] 2. Calculate the “growth score” of each candidate based on the experimental data
[0039] like Figure 2 As shown, the quantitative evaluation results of the effects of candidate drugs on the bacterial growth cycle, i.e., the comparison of "growth scores", are presented in an intuitive bar chart format;
[0040] like Figure 2 (a) (control group without candidate treatment): This part shows the "growth score" obtained by natural bacterial growth without the addition of any candidate drug treatment. This score is used as a baseline for subsequent evaluation of the specific effects of different candidate drugs on the bacterial growth cycle. This score reflects the growth potential and cycle characteristics of bacteria under standard culture conditions;
[0041] like Figure 2 (b) (different candidate treatment groups): This part shows in detail the "growth scores" obtained by bacteria after being treated with various candidate drugs. The bar height corresponding to each candidate drug represents the quantitative result of its comprehensive impact on the bacterial growth cycle at a specific concentration. By comparing the "growth scores" of different candidate drugs, it is possible to intuitively identify which drugs have a significant inhibitory effect, a promoting effect, or no significant effect on bacterial growth, thereby providing an important basis for drug screening.
[0042] 3. Use the “growth score” to cluster or rank the candidates, and prioritize high-scoring drug candidates for further research and development;
[0043] like Figure 3 As shown, cluster analysis was used to deeply evaluate the “growth score” patterns of drug candidates;
[0044] The "growth score" of each candidate drug is treated as an independent observation point, and the cluster analysis algorithm groups them according to the similarity between these scores. Through this process, we can observe which drugs have similar effect patterns in affecting the bacterial growth cycle and which drugs exhibit unique effect characteristics.
[0045] 4. Verification
[0046] The direct effect of candidate drugs on the viability of mycobacteria under the "growth score" screening system;
[0047] Based on the direct effect of effective drugs identified by the candidate drug "growth score" screening system on the survival ability of mycobacteria, the screening system comprehensively evaluates the impact of candidate drugs on each stage of the bacterial growth cycle and quantifies the "growth score", thereby accurately identifying drugs with potential antibacterial activity;
[0048] like Figure 4 The left side shows the natural growth state and survival ability of mycobacteria in the absence of drugs. This part serves as a control group and provides a benchmark reference for the subsequent evaluation of drug effects. By monitoring the survival rate or colony forming unit (CFU) number of mycobacteria within a certain period of time, its growth characteristics without external intervention can be clearly understood;
[0049] like Figure 4 The right side shows the direct inhibitory effect of the screened effective drugs on the survival ability of mycobacteria in the presence of candidate drugs. The figure compares the survival results of mycobacteria after treatment with different concentrations of candidate drugs, intuitively showing the dose-dependent relationship between drug concentration and antibacterial effect;
[0050] By comparison Figure 4 On the left and right sides, it can be clearly seen that the drug has a significant impact on the survival ability of mycobacteria, verifying the effectiveness of the "growth score" screening system.
[0051] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the spirit of the present invention.
Claims
1. A method for screening drug candidates based on growth score, characterized in that: The candidates are ranked using a "growth score" calculated by quantifying their effect on the cell growth cycle to prioritize drug candidates with high scores.
2. The growth score-based drug candidate screening method according to claim 1, characterized in that: The specific steps are as follows: S1: Decomposing the geometry of bacterial growth curves S11: Lag period: During this period, the bacteria adapt to the new environment, and the growth rate is extremely low. The curve is a nearly horizontal straight line. By fitting the data of this stage, the length of the lag period and its corresponding growth rate can be determined. S12: Exponential growth phase: The bacterial growth rate is constant, and the curve is a straight line with a constant slope. The rapid proliferation ability of bacteria is evaluated by calculating the slope of the straight line in this phase; S13: Stable phase: resources are limited, the growth rate slows down to zero, the curve tends to be horizontal, and the duration of this phase and the final cell density are recorded; S2: Definition and calculation of "growth score" S21: The geometric shapes of the above three stages are considered as a collection of bacterial growth trajectories, where the area (or integral) of each stage represents the cumulative growth of the number of cells in that stage; S22: Define "growth score" as the sum of the areas (or integrals) of all stages during the entire growth cycle. This score comprehensively reflects the overall growth capacity of bacteria from inoculation to the stationary phase; S23: Through mathematical formulas or algorithms, the geometric parameters of each stage are converted into calculation factors of "growth score" to ensure the accuracy and comparability of the score; S3: Drug Screening Applications S31: Plot bacterial growth curves under different drug treatment conditions and perform geometric shape decomposition; S32: Calculate the growth score under each drug treatment and compare it with the control group without drug treatment; S33: Based on the degree of reduction in the growth score, the inhibitory effect of the drug on bacterial growth is evaluated and drug candidates with potential therapeutic effects are screened out.
3. The growth score-based drug candidate screening method according to claim 2, characterized in that: The geometric parameters in S23 include length, slope and final density.