用SPSS軟件計算新藥的LD50新藥急性毒性試驗的LD50測定是臨床前安全評價的重要內容，也是各類藥品(化學、中藥和生物制品)注冊時必需申報的資料。LD50的計算有10余種方法,80年代出現的“Bliss法半數效量電子計算器程序運算法”曾得到廣泛應用，但隨著時代的發展，尤其是電子計算機的日益普及，該方法也顯現出操作比較繁雜、無法人機對話及無結果輸出等不足。近期雖有人報道直接用計算機計算LD50的方法，但需要預先進行編程?，F介紹直接用SPSS(StatisticalPackagefortheSocialScience)軟件進行機率單位加權回歸法(Bliss法)計算LD50的方法，其具有簡單、快速、直觀和方便的特點。以某藥的急性毒性試驗所得數據(見表1)為例。對用SPSS軟件計算LD50的過程進行介紹。表1某藥急性毒性試驗數據劑量(mg/kg)動物總數死亡數192.00101240.00103300.00106375.00108468.75109輸人表1數據的過程如下:運行SPSS10.0forWindows，進人SPSS數據編輯器后，首先激活變量表(VriableView)定義變量，在變量名(name)下輸人“劑量”、“動物數”和“死亡數”，并將“劑量”的小數點位數(Decimal)設為“2"“動物數”和“死亡數”的小數點位數設為“0"。然后進人數據表(DataView)，根據表頭提示，依次輸入各組數據。1.2參數選擇在數據表主菜單中，選擇Analyze—Regression—Probit，調出ProbitAnalyze對話框，只需將“劑量”選入“Covariates”欄中，“死亡數"選入“Responsefrequency”欄中，“動物總數"選入“Totalobserved"欄中，在"Transform"欄中，選擇“logbase10"(以10為底的對數轉換)，其它保持默認選項(圖2)。然后單擊“0K"鈕即完成整個操作過程。2結果完成上述操作，SPSS會自動顯示全部分析結果(SPSSViewer)，其中包括:回歸方程參數表，不同劑量的實驗值和預期值概率表，從0.01到0.99死亡率的劑量(包括0.50，即LD50)及95%可信區間表。以及對數單位與概率單位的關系曲線圖。輸出結果如下：PROBIT死亡數OF動物總數WITH劑量/LOG10/MODELPROBIT/PRINTFREQCI/CRITERIAP(0.15)ITERATE(20)STEPLIMIT(.1).ProbitAnalysisNotesOutputCreated2008-05-26T17:11:06.156CommentsInputActiveDatasetDataSetOFilter<none>Weight<none>SplitFile<none>NofRowsinWorkingDataFile5MissingValueHandlingDefinitionofMissingUser-definedmissingvaluesaretreatedasmissing.CasesUsedStatisticsarebasedonallcaseswithvaliddataforallvariablesinthemodel.SyntaxPROBIT死亡數OF動物總數WITH劑量/LOG10/MODELPROBIT/PRINTFREQCI/CRITERIAP(0.15)ITERATE(20)STEPLIMIT(.1).ResourcesProcessorTime0:00:00.703ElapsedTime0:00:00.984[DataSet0]WarningsRelativeMedianPotencyEstimatesarenotdisplayedbecausethereisnogroupingvariableinthemodel.DataInformation

NofCasesValid5RejectedMissing0LOGTransformCannotbeDone0NumberofResponses>NumberofSubjects0ControlGroup0ConvergenceInformationNumberofIterationsOptimalSolutionFoundPROBIT8YesParameterEstimatesParameterEstimateStd.ErrorZSig.95%ConfidenceIntervalLowerBoundUpperBoundPROBITa劑量6.7561.6864.007.0003.45110.061Intercept-16.5994.167-3.983.000-20.766-12.431a.PROBITmodel:PROBIT(p)=Intercept+BX(CovariatesXaretransformedusingthebase10.000logarithm.)Chi-SquareTestsChi-SquaredfaSig.PROBITPearsonGoodness-of-FitTest.2343.972bStatisticsbasedonindividualcasesdifferfromstatisticsbasedonaggregatedcases.Sincethesignificancelevelisgreaterthan.150,noheterogeneityfactorisusedinthecalculationofconfidencelimits.CellCountsandResiduasNumber劑量NumberofSubjectsObservedResponsesExpectedResponsesResidualProbabilityPROBIT12.2831011.206-.206.12122.3801033.026-.026.30332.4771065.548.452.555

42.57410542.5741052.671107.860 .1409.261 -.261.786.926Probability95%ConfidenceLimitsfor劑量95%ConfidenceLimitsforlog（劑量）aEstimateLowerBoundUpperBoundEstimateLowerBoundUpperBoundPROBIT0.01129.54457.746173.5082.1121.7622.2390.02142.15569.071185.1922.1531.8392.2680.03150.78677.353193.0802.1781.8882.2860.04157.62384.212199.2832.1981.9252.2990.05163.41290.221204.5132.2131.9552.3110.06168.50695.657209.1042.2271.9812.3200.07173.103100.679213.2422.2382.0032.3290.08177.326105.387217.0432.2492.0232.3370.09181.256109.847220.5822.2582.0412.3440.1184.950114.108223.9132.2672.0572.3500.15201.064133.407238.5612.3032.1252.3780.2214.866150.741251.3942.3322.1782.4000.25227.460167.039263.5132.3572.2232.4210.3239.397182.738275.5472.3792.2622.4400.35251.018198.054287.9782.4002.2972.4590.4262.566213.084301.2632.4192.3292.4790.45274.243227.851315.8882.4382.3582.5000.5286.243242.336332.4012.4572.3842.5220.55298.768256.532351.4292.4752.4092.5460.6312.056270.487373.6972.4942.4322.5730.65326.412284.347400.1022.5142.4542.6020.7342.256298.389431.8732.5342.4752.6350.75360.218313.051470.8992.5572.4962.6730.8381.331329.015520.4302.5812.5172.7160.85407.508347.456586.7932.6102.5412.7680.9443.012370.835684.8342.6462.5692.8360.91452.041376.556711.1712.6552.5762.852

0.92462.059382.815741.0402.6652.5832.8700.93473.331389.757775.4542.6752.5912.8900.94486.245397.590815.9222.6872.5992.9120.95501.403406.637864.8272.7002.6092.9370.96519.818417.435926.2532.7162.6212.9670.97543